Patent Grant
11856011
Embodiments of the disclosure relate to the field of cybersecurity. More specifically, one embodiment of the disclosure relates to a system and method for improved detection of cyberattacks through the sharing of data between cybersecurity systems operating within an enterprise network.
Network devices provide useful and necessary services that assist individuals in business and in their everyday lives. In recent years, a growing number of cyberattacks are being conducted on all types of network devices. In some cases, these cyberattacks are orchestrated in an attempt to gain access to content stored on one or more network devices (e.g., endpoints, cybersecurity systems, etc.). Such access is for illicit (i.e., unauthorized) purposes, such as spying or other malicious or nefarious activities. For protection, cybersecurity systems may be deployed at a local network or on an endpoint in efforts to detect a cyberattack caused by a malicious object.
Over the years, companies have deployed many different types of cybersecurity systems as part of their network-based, malware protection solution. One type of cybersecurity system includes a network appliance, which performs a two-phase approach for detecting malware contained in network traffic. This two-phase approach includes a static phase and a dynamic phase. During the static phase, network traffic is examined (without execution), for example, for signature-based matches against a library of known malware, and/or for communication-based protocol anomalies or other characteristics upon which a cybersecurity classification may be based. During the dynamic phase, a virtual machine deployed within the cybersecurity system executes objects obtained from the network traffic and monitors the behaviors of each object during execution where information regarding each monitored behavior may also be referred to as an “event” and may be used in cybersecurity classification of the object.
Alternatively, another type of cybersecurity system involves the installation of a cybersecurity agent (hereinafter, “agent”) within an endpoint to be protected. For this deployment, the agent is responsible for monitoring and locally storing selected behaviors. Herein, with respect to an endpoint, a behavior includes a task or activity that is conducted by a software component running on the endpoint and, in some situations, the activity may be undesired or unexpected indicating a cyberattack is being attempted, such as a file being written to disk, a process being executed or created, or an attempted network connection.
Currently, cybersecurity detection platforms that deploy a plurality of cybersecurity systems may perform malware detection by analyzing objects according to a particular focus of each cybersecurity system. For example, a first cybersecurity system, operating as a first line of defense, may be an email appliance directed to the analysis of incoming emails and associated objects (i.e., attachments, embedded Uniform Resource Locators “URLs,” etc.). Additionally, a second cybersecurity system, operating as a second line of defense, may be an agent running as a daemon process on an endpoint to which an email may be directed, where the agent analyzes the activities resulting from processing of the email by an email application running on the endpoint in real-time. As the first and second cybersecurity systems perform different analyses, each cybersecurity system may include various logic components and apply different rule sets. Thus, analysis of the same object (e.g., the incoming email) by the first and second cybersecurity systems may result in different outcomes, i.e., verdicts of maliciousness, which may be a determination of a classification of malicious, suspicious, benign or indeterminate. Further, the first and second cybersecurity systems typically operate within separate “silos.”
Embodiments of the disclosure are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings, in which like references indicate similar elements and in which:
I.Overview Summary
In embodiments of the invention, objects in transit in network traffic or contained in emails may be analyzed by multiple cybersecurity systems as the objects enter and traverse an enterprise network. Contextual data from previously performed analyses are shared among the cybersecurity systems so as to enhance the analysis and classification of the objects in later analyses. Absent such sharing, some analyses may fail to account for the vector, e.g., manner, by which the object entered the network (“the origination of the object”) and the results of prior analyses of the object. By sharing the origination of the object and other contextual data of prior analyses, later analyses of the object may more efficiently utilize system resources, and more effectively detect cyberattacks by reducing false negative (FN) and/or false positive (FP) classifications. In addition, by the sharing and reporting of the combined contextual data from among the cybersecurity systems, these embodiments can provide more information regarding attack life cycles, including the kill chain of cyberattacks so as to enhance remediation efforts.
In general, one embodiment of the disclosure relates to a malware analysis data sharing system that is designed to facilitate the sharing of contextual data between a first cybersecurity system and a second cybersecurity system in order to improve malware analyses performed by the second cybersecurity system. For this embodiment of the disclosure, the malware analysis data sharing system includes a plurality of cybersecurity systems (e.g., cybersecurity appliances and/or agents) deployed within or coupled to an enterprise network. Each cybersecurity system is configured to conduct analyses of objects to determine whether any of the objects may be associated with malware.
As described below, according to one embodiment of the disclosure, a first cybersecurity system performs a first malware analysis on the object and generates contextual data (generally referred to as “context information”) following, or concurrent with, the analysis. Herein, the term “concurrent” means at least partially overlapping in time. The “context information” may refer to information detailing the performance of a malware analysis, which may include a result of the analysis, often referred to as a score or verdict, one or more identifiers (e.g., hashes) of analyzed objects, identifiers of processes running objects of interest, one or more rules implicated during the analysis, and/or origination information of the one or more analyzed objects. Herein, this context information may be provided in different formats such as the actual contextual data, a representation of the actual contextual data as a plurality of bits or pointers each representing a portion of the context information in lieu of the actual contextual data itself, or a combination thereof.
In one embodiment, upon completing the first malware analysis, the first cybersecurity system modifies the object to include a representation of the context information. The modification of the object with a representation of the context information facilitates and automates the sharing of the context information with a second cybersecurity system. Herein, modification of an object to include a representation of the context information may be referred to as “active sharing.” For example, the first cybersecurity system may modify the header of an email object to include the context information. In particular, the first cybersecurity system may perform a first malware analysis that differs from a second malware analysis performed by the second cybersecurity system. In one embodiment, the first cybersecurity system may be a network appliance that performs an analysis of objects received via email including emails, attachments, embedded objects, URLs, etc., while the second cybersecurity system may be a software agent processing on an endpoint within the enterprise network configured to monitor activities at the endpoint. As the analyses performed by the first and second cybersecurity systems may differ in rules or logic applied as well as behaviors monitored, the cybersecurity systems may not gather the same information during their analyses.
Therefore, by providing context information associated with its malware analysis, the first cybersecurity system can greatly improve and assist with the malware analysis performed by the second cybersecurity system. Specifically, the second cybersecurity system, e.g., an agent installed on an endpoint, may improve its analysis by either (i) determining the origination of the object, and prioritizing the analysis of the object to increase the scrutiny of the analysis, such as by activating certain monitors, adjusting thresholds for determining maliciousness, applying a modified rule set during its analysis for example, or (ii) maintaining a more detailed event log and performing a more detailed preliminary detection, if the agent is so-equipped. In particular, one or more thresholds may be used by the cybersecurity systems to determine levels of maliciousness (e.g., suspicious or malicious) based on comparing scores associated with the activities, behaviors or characteristics observed and other context information, and may be modified based on the context information. The logic components of the agent that assist in and perform the prioritizing are discussed in further detail with respect to
In some embodiments, cybersecurity systems implemented as appliances (such as those directed to analyzing network traffic or email traffic) may perform an analysis including (i) static and/or dynamic analyses, as described above, (ii) monitoring for selected events or combinations of events conducted by the object (or the application in which the object is processed, and/or the virtual machine during such processing), (iii) detecting occurrences of any of these monitored events or combinations of events, and (iv) classifying the observed events with respect to maliciousness. In some embodiments, a network appliance monitors traffic entering and, sometimes, exiting, a trusted area of a protected network (e.g., at least a portion of an enterprise network), and, thus is disposed to monitor the traffic at the periphery of the network. Additionally, in some embodiments, an email appliance is situated behind the exchange server and configured to monitor email traffic entering, and at times, exiting the network. The logic components of the appliances that assist in and perform the prioritizing are discussed in further detail with respect to
The cybersecurity systems may also perform a correlation or classification phase based on its own malware analysis and, in the case of the second cybersecurity system, the context information provided by the first cybersecurity system, to determine whether the object is malicious, benign or indeterminate (i.e., neither malicious nor benign), which may be represented by a score. Additionally, the second cybersecurity system may generate and issue a report, directly or indirectly via another cybersecurity system, to one or more endpoints, an administrator of the enterprise network and/or a cybersecurity specialist such that the report is based on its malware analysis, and optionally, the context information provided by the first cybersecurity system.
The malware analysis data sharing system may also facilitate a remediation process when an object is determined to be malicious. Based on the context information, the second cybersecurity system, or another logic component within the enterprise system, may determine the origination of a malicious object and employ retroactive detection logic to facilitate remediation of any infected devices (e.g., endpoints and/or network appliances). The retroactive detection logic may utilize the context information to trace the path through which the malicious object traveled within the enterprise network. By determining the path through which the malicious object traveled, the retroactive detection logic can initiate remediation efforts on any affected network device (e.g., quarantine the device on the network and/or the object within the device). In addition, the retroactive detection logic may flag any rules during past analyses that failed to lead to classifying the object as malicious and transmit the flagged rule(s), directly or indirectly, to an administrator and/or cybersecurity specialist for review and/or updating to improve efficacy in future detections by the updated rule set. Alternatively, this can be performed automatically. Additionally, in some embodiments when an agent determines an email object is malicious, the agent may prevent the user from opening an attachment or activating a URL, if applicable. Similarly, when a network cybersecurity system determines an outbound network request or an inbound response is associated with malware or a cyberattack, the network cybersecurity system can prevent the network request from being transmitted and/or prevent the inbound response from reaching the corresponding endpoint. Further, any cybersecurity system may, upon determining an object is malicious, transmit a notification to a threat management system, which notifies other cybersecurity systems and endpoints that received the same object, e.g., an email.
Additionally, in some embodiments, the first cybersecurity system may provide the context information based on the analysis of one or more objects to a non-transitory storage location communicatively coupled to the enterprise network, often referred to as a “cybersecurity intelligence hub.” Additionally, the context information may be provided to a management system, e.g., a logic component generally configured to correlate data received from multiple cybersecurity systems. Receipt of context information from multiple cybersecurity systems enables the management system to (i) determine whether the same malware appears to be present at different cybersecurity systems (i.e., evidence of a malware attack) and (ii) consolidate at least a portion of the data in order to provide a holistic view of the malware attack. One embodiment of a management system is described in more detail in U.S. Pat. No. 9,311,479, titled, “Correlation and Consolidation of Analytic Data For Holistic View of A Malware Attack” by Manni et al., filed Apr. 12, 2016, the entire contents of which are hereby incorporated by reference.
In some embodiments in which the context information is provided to a cybersecurity intelligence hub (described below), the first cybersecurity system may provide the one or more analyzed objects to the second cybersecurity system without the context information. The second cybersecurity system may then retrieve the context information from the cybersecurity intelligence hub and perform its malware analysis in accordance with the context information. Herein, the use of the cybersecurity intelligence hub for storage and retrieval of context information may be referred to as “passive sharing.” Further, in some embodiments, a combination of active sharing and passive sharing are utilized to share context information generated by a first cybersecurity system with a second cybersecurity system.
In some embodiments, as will be discussed in detail below, a first cybersecurity system performs a first malware analysis on one or more objects of an incoming message, e.g., an email, and generates first context information that is provided, via active or passive sharing, to a second cybersecurity system, and optionally, a cybersecurity intelligence hub. The cybersecurity intelligence hub is configured to perform monitoring on a global scale, while reducing the overall network throughput requirements and mitigating repetitive analytics on identical objects. This allows for better platform scalability without adversely affecting the currency or relevancy of stored metadata within the cybersecurity intelligence hub. Hence, the cybersecurity intelligence hub may operate as (i) a central facility connected via a network to receive context information from the cybersecurity systems; (ii) an intelligence analytics resource to analyze the received context information, including results from an analysis of context information or object received from disparate sources, and store the analysis results with (or cross-referenced with) the received context information; and/or (iii) a central facility serving as a distribution hub connected via a network to distribute the stored context information to the consumers.
The second cybersecurity system then performs a second malware analysis, in some embodiments, at least in part in accordance with the first context information, and generates a second context information corresponding to the second malware analysis. The second context information may then be provided, via active or passive sharing, to the cybersecurity intelligence hub and a third cybersecurity system, which may perform a third malware analysis in accordance with at least the second context information such as analyzing context information of an outgoing message, e.g., outbound network request, and updating the header of the outgoing message to reflect both its origination and any an operation that triggered the outgoing message. In one embodiment, the third cybersecurity system may also retrieve the first context information from the cybersecurity intelligence hub and perform the third malware analysis in light of both the first and second context information.
It should also be noted that, although the examples discussed herein relate to receipt of an email object, a network request and, in some instances, an inbound response, other object types may be analyzed in a similar manner by one or more of the cybersecurity systems disclosed herein. For example, a cybersecurity system may be deployed as an agent on an endpoint to analyze an object stored on a removable storage device, such as for example a USB device. The agent may utilize the origination of the object to determine a specific rule set for analysis of the object, and remediation, if necessary. For example, if the object retrieved from a USB device launches a script, such activity may be deemed at least suspicious based on the object's origination.
II. Terminology
In the following description, certain terminology is used to describe various features of the invention. For example, each of the terms “logic,” “engine,” and “component” may be representative of hardware, firmware or software that is configured to perform one or more functions. As hardware, the term logic (or component) may include circuitry having data processing and/or storage functionality. Examples of such circuitry may include, but are not limited or restricted to a hardware processor (e.g., microprocessor, one or more processor cores, a digital signal processor, a programmable gate array, a microcontroller, an application specific integrated circuit “ASIC”, etc.), a semiconductor memory, or combinatorial elements.
Additionally, or in the alternative, the logic (or component) may include software such as one or more processes, one or more instances, Application Programming Interface(s) (API), subroutine(s), function(s), applet(s), servlet(s), routine(s), source code, object code, shared library/dynamic link library (d11), or even one or more instructions. This software may be stored in any type of a suitable non-transitory storage medium, or transitory storage medium (e.g., electrical, optical, acoustical or other form of propagated signals such as carrier waves, infrared signals, or digital signals). Examples of a non-transitory storage medium may include, but are not limited or restricted to a programmable circuit; non-persistent storage such as volatile memory (e.g., any type of random access memory “RAM”); or persistent storage such as non-volatile memory (e.g., read-only memory “ROM”, power-backed RAM, flash memory, phase-change memory, etc.), a solid-state drive, hard disk drive, an optical disc drive, or a portable memory device. As firmware, the logic (or component) may be stored in persistent storage.
Herein, a “communication” generally refers to related data that is received, transmitted, or exchanged within a communication session. The data may include a plurality of packets, where a “packet” broadly refers to a series of bits or bytes having a prescribed format. Alternatively, the data may include a collection of data that may take the form of an individual or a number of packets carrying related payloads, e.g., a single webpage received over a network.
The term “computerized” generally represents that any corresponding operations are conducted by hardware in combination with software and/or firmware.
The term “agent” generally refers to a module of software installed on a target system (e.g., an endpoint, or other network device) that monitors processing activities and interacts with the target system. Agents gather information about multiple aspects of the target system. Additionally, agents, in some embodiments and responsive to the activities in accordance to monitoring rules followed by the agent, store the monitored activities as events in an event log and permit remote retrieval, on a push or pull basis, of the contents of the event log. The agent may be configured to either communicate (via the endpoint in which it is installed) over a computer network, or to read and write all relevant configuration information and acquired data to a computer storage medium, such as a hard drive or removable read/write media (USB drive, etc.). In one embodiment, the agent is built in a modular fashion. The ability to gather a particular piece of data from a target system (e.g. a list of running processes on the target system or a log file or timeline) is implemented as separate modules of software and loaded by the agent. This allows for easy adaptation of the agent to different environments that have specific requirements for data collection.
According to one embodiment of the disclosure, the term “malware” may be broadly construed as any code, communication or activity that initiates or furthers a cyberattack. Malware may prompt or cause unauthorized, anomalous, unintended and/or unwanted behaviors, or in some situations, more specifically, operations. constituting a security compromise of information infrastructure (herein, the term “operation” may be considered one example of a behavior). For instance, malware may correspond to a type of malicious computer code that, as an illustrative example, executes an exploit to take advantage of a vulnerability in a network, network device or software, for example, to gain unauthorized access, harm or co-opt operation of a network device or misappropriate, modify or delete data. Alternatively, as another illustrative example, malware may correspond to information (e.g., executable code, script(s), data, command(s), etc.) that is designed to cause a network device to experience anomalous (unexpected or undesirable) behaviors. The anomalous behaviors may include a communication-based anomaly or an execution-based anomaly, which, for example, could (1) alter the functionality of a network device executing application software in an atypical manner; (2) alter the functionality of the network device executing that application software without any malicious intent; and/or (3) provide unwanted functionality which may be generally acceptable in another context.
The term “object” generally relates to content (or a reference to access such content) having a logical structure or organization that enables it to be classified for purposes of analysis for malware. The content may include an executable (e.g., an application, program, code segment, a script, dynamic link library “d11” or any file in a format that can be directly executed by a computer such as a file with an “.exe” extension, etc.), a non-executable (e.g., a storage file; any document such as a Portable Document Format “PDF” document; a word processing document such as Word® document; an electronic mail “email” message, web page, etc.), or simply a collection of related data. Additionally, the term object may refer to an instance of an executable that is executing (“a process”). In one embodiment, an object may be a URL or list of URLs. The object may be retrieved from information in transit (e.g., one or more packets, one or more flows each being a plurality of related packets, etc.) or information at rest (e.g., data bytes from a storage medium).
The term “network device” may be construed as any electronic computing system with the capability of processing data and connecting to a network. Such a network may be a public network such as the Internet or a private network such as a wireless data telecommunication network, wide area network, a type of local area network (LAN), or a combination of networks. Examples of a network device may include, but are not limited or restricted to, an endpoint (e.g., a laptop, a mobile phone, a tablet, a computer, etc.), a standalone appliance, a server, a router or other intermediary communication device, a firewall, etc. For convenience, and by way of illustration, the description that follows will often describe the agent and its operation in terms of being located on an endpoint rather than the more general case of a network device. Moreover, the description may describe communication as being between an agent and one or more external devices or components thereof though it will be understood that such communication is effected via the network device in which the agent is installed.
The term “rules” generally refers to software or data that is used in detection of malware or cyberattacks. According to one embodiment of the disclosure, each rule is configured to specify a conditional logic statement or programmatic entity used in a cybersecurity system during operation in detecting and classifying cyberattacks. Rules may be received and maintained by the cybersecurity system in files, libraries, directories, or other modular programmatic structures, or may be integrated into logic running within the cybersecurity systems for example such as in their operating software. Importantly, in embodiments of the invention, the cybersecurity rules may be individually modified or a set of rules may be selected and/or modified (changed, added, subtracted, etc.) for use in the cybersecurity engine during runtime to adjust operability of the cyber-security system and influence cybersecurity verdicts.
For instance, rules may be logic for use in detecting a cyberattack, each capable of being represented as a logical expression for example, an “if this, then that” conditional statement where “this” represents a condition and “that” represents the conclusion (e.g., malicious or not malicious, etc.). A condition may encompass, for example, a signature, heuristic, pattern, string or value. The conclusion is applied when the condition is met, for example, by one or more observed characteristics, behaviors or activities. For convenience in terminology, each rule may also represent only the “if this” portion of the conditional statement, with the balance of the logical expression (“then that”), implemented by the operating software of the cybersecurity engine that consumes the rule. Hence, the term “implicated rules,” as used herein, refers to the one or more specific rules applied in reaching a verdict, reflecting observed characteristics, behaviors and activities and the conclusions drawn from them based on the logical expressions.
As another illustrative example, a rule may provide configuration information containing parameter values for configuring the operating software of the cybersecurity system, such as, for example, threshold values used in detection (e.g., specifying the threshold for suspiciousness and maliciousness). Different types of analyses may be configured to utilize different types of rules. For instance, signature-based rules are used in determining whether a hash (or other signature of an event) matches a stored library of signatures (e.g., exploit, vulnerability or fingerprint-type signatures). Likewise, protocol anomaly rules determine whether an event matches a stored library of protocol-specific requirements and/or anomalies (e.g., communication header anomalies with respect to HTTP) while execution anomaly rules determine whether, during execution of an object, observed activities and behaviors are expected or anomalous. Classification rules determine verdicts, for example, based on weightings or scores for observed characteristics, activities and behaviors. In addition, cybersecurity rules sets include prioritization rule sets and remediation rule sets, as described below.
Herein, rules may be stored in a rules store (e.g., a repository) in persistent memory of a cybersecurity system and are typically updated frequently (periodically or aperiodically) in light of the prevailing threat landscape. The rule updates may be distributed to the cybersecurity systems in security content files or libraries, originating from, for example, a manufacturer or other source charged with assuring the security content used by the cybersecurity systems are current. Such rule updates are in contrast to rule set selection (where plural alternative rule sets may be maintained by the cybersecurity system), or rule modification by the cybersecurity system, based on the contextual information of a prior analysis, as described herein with regard to embodiments of the invention.
Finally, the terms “or” and “and/or” as used herein are to be interpreted as inclusive or meaning any one or any combination. Therefore, “A, B or C” or “A, B and/or C” mean “any of the following: A; B; C; A and B; A and C; B and C; A, B and C.” An exception to this definition will occur only when a combination of elements, functions, steps or acts are in some way inherently mutually exclusive.
As this invention is susceptible to embodiments of many different forms, it is intended that the present disclosure is to be considered as an example of the principles of the invention and not intended to limit the invention to the specific embodiments shown and described.
III. General Architecture
Referring to
According to
Further, upon receiving the modified object 106 with the context information 108, the second cybersecurity system 104 performs a second malware analysis on the object 106 in light of the context information 108. As will be described in detail below, the second cybersecurity system 104 may determine the origination of the object 106 from the context information 108. As one example, in an embodiment in which the object 106 is an email object received by an email cybersecurity appliance monitoring emails entering the network, the origination refers to “email”.
Based on the origination of the object 106, and optionally other information included in the context information 108, the second cybersecurity system 104 may prioritize, e.g., modify or tailor, its malware analysis of the object 106. For example, the second cybersecurity system 104 may typically employ logic that utilizes a set of predetermined rules (or models, as discussed below) in its malware analysis and/or classification phase. However, based on the origination of the object 106, the second cybersecurity system 104 may employ the logic utilizing a modified set of rules (or models). Therefore, the second cybersecurity system 104 may utilize the context information 108 to tailor a malware analysis and/or classification phase to the object 106, taking into account the origination of the object 106 and one or more rules (or models) implicated in the malware analysis performed by the first cybersecurity system 102 (i.e., rules triggered during analysis that factored in the determination of the verdict). Specifically, the tailored malware analysis and classification phase leads to fewer false positives and false negatives, thereby providing an improved malware detection process.
As a useful construct, the rules used in the cybersecurity system 102 and 104 may be categorized as follows: (1) detection rules, (2) correlation rules, and (3) remediation rules. Detection rules are used to determine whether an alert or other report of one or more cybersecurity events should be generated and issued when the condition or conditions within a rule matches observed characteristics, activities, and behaviors. One example of detection rules differentiates between normal internet activities and malicious activities indicative of an intrusion into a protected network. Information regarding these activities, when detected, constitute events stored in an event log, and it is these events to which the rules are applied. Hence, the detection rules generally “model” the many events that cybersecurity systems may capture. Correlation rules are used to compare one or more events (e.g., patterns or sequences) with patterns of known malicious or non-malicious activities to classify the observed activities, e.g., as malicious, benign, or indeterminate, and thus ascertain whether a cyberattack occurred or is occurring. Remediation rules are used to determine what remedial measures, if any, should be activated to prevent, block or mitigate a cyberattack, which measures may automatically be deployed or and which only upon approval from a network or security administrator.
For example, one or more of the cybersecurity systems 102 and 104 (e.g., cybersecurity system 102) may be deployed as an appliance that is communicatively coupled to receive and analyze incoming messages. As an appliance, the cybersecurity system 102 includes logic being physical components that analyze incoming message for malware, such as a processor and memory including one or more virtual machines, software (e.g., OS(es), application(s), plug-in(s), etc.) to instantiate each of the virtual machines, and monitoring logic to monitor for certain behaviors conducted by an object running in a virtual machine (VM).
Further, one or more of the cybersecurity systems 102 and 104 (e.g., cybersecurity system 104), may be deployed as a virtual device, namely a software (daemon) agent to detect cyberattacks, which may operate in the foreground (or background as the daemon) of a network device (e.g., an endpoint). An agent cybersecurity system installed on an endpoint may monitor activities processing on the endpoint looking for indicators of compromise (IoCs), which may be discovered by the agent cybersecurity system during monitoring of operations performed by the endpoint.
Each of the cybersecurity systems 102 and 104 (e.g., cybersecurity system 102) may be deployed on-premises (e.g., as an edge network device for the local network, as a network device within the local network, etc.) to detect and analyze incoming objects propagating into or through the local network for malware, etc., or deployed proximate to an exchange server to monitor email traffic attempting to enter the local network or being sent to a protected network device. Alternatively, although not shown, each of the cybersecurity systems 102 and 104 may be deployed as a cloud-based solution in which the incoming objects (or a representation thereof) are captured at the local network and submitted to at least one of the cloud-based cybersecurity systems 102 and 104 (e.g., or a copy of the incoming objects may be submitted). Additionally, at least one of the cybersecurity systems 102 and 104 (e.g., cybersecurity system 104) may be deployed at an endpoint as a software agent operating in the background to analyze and monitor for certain behaviors by the object.
It is contemplated that, although described within or coupled to an enterprise network deployment, the cybersecurity systems 102 and 104 may be deployed as systems within a public or private cloud service or systems deployed and communicatively coupled over a public network (e.g., internet). In such systems, customers may subscribe to cybersecurity services, and pay according to the services received. In such cloud-based deployments, based on the context information 108 from the first cybersecurity system 102 (e.g., origination), the second cybersecurity system 104 may modify its malware analysis to accommodate different quality of service (QoS) levels based on subscription tier or other subscription-related factors associated with the customer of a cybersecurity service, the service being provided, at least in part, by the first cybersecurity system 102. For instance, the second cybersecurity system 104 may support a different (quicker) response time or a different set of predetermined rules in its malware analysis (i.e., more comprehensive analysis) based on the context information 108 provided and based as well on the subscription factors for the customer involved (e.g., the customer to whom the email or webpage, as the case may be, was directed). The rules may be modified, e.g., customized, pursuant to a request by or in light of the threat landscape confronting the customer. In addition, one or more thresholds used by the cybersecurity systems to determine levels of maliciousness (e.g., suspicious or malicious) based on comparing scores associated with the behaviors and characteristics observed and other context information, may be modified on a per-customer basis.
IV. System Architecture
Referring now to
As is seen in the illustration of
The first cybersecurity system 202 determines a verdict as a result of the first malware analysis (suspicious, malicious, benign or indeterminate) or a score reflecting the maliciousness of the object. When the first malware analysis results in a verdict of maliciousness (or a score exceeding a predetermined threshold), the first cybersecurity system 202 may generate and issue a report to the management system 208 and/or the second and third cybersecurity systems 204 and 210. The report may include a visual representation of the first context information 218 (e.g., one or more display screens, printed report, etc.).
When the first malware analysis results in a verdict other than malicious (suspicious, benign or indeterminate) or the score does not exceed a predetermined threshold, the first cybersecurity system 202 provides at least the first context information 218 to the second cybersecurity system 204 through either active sharing and/or passive sharing, as will be discussed below. Additionally, the first cybersecurity system 202 may optionally provide the context information to the management system 208 for aggregation and correlation with context information 218 or other data received from other cybersecurity systems. Although not illustrated in
In one embodiment, the first cybersecurity system 202 may actively share the first context information 218 with the second cybersecurity system 204. For example, the active sharing may include modification of the object 216 to include at least a representation of the first context information 218. In some embodiments, the object 216 may be modified by altering the header of the object 216 to include the first context information 218, represented by a vector comprising a plurality of bits for example. The plurality of bits may enable the second cybersecurity system 204 to determine the content of the context information 218. As discussed above, altering of the header of the object 216 may include the addition of a X-header where the object is an email. However, modification of the object 216 to include the context information 218 is not limited to addition of a X-header or even generally modification of the header of the object 216. The disclosure contemplates that active sharing should be broadly construed as any modification of the object 216 to include the context information 218.
Additionally, as referenced above, the context information 218, or a representation thereof such as a plurality of bits, may be encoded when provided to the second cybersecurity system 204. The encoding of the context information provides resource-saving advantages by compressing the data and also obscures the information so as not to be easily intercepted by malware, malware writers and/or alternative cybersecurity systems. Malware may use the header information to evade detection.
Upon modifying the object 216 to include the first context information 218, the first cybersecurity system 202 transmits the modified object 216 to the second cybersecurity system 204. Thus, by receiving the modified object 216, the second cybersecurity system 204 receives the first context information 218.
As an alternative, or in addition, to active sharing, the second cybersecurity system 204 may obtain the first context information 218 via passive sharing techniques. In one embodiment, the first cybersecurity system 202 may passively share the first context information 218 with the second cybersecurity system 204 by uploading the first context information 218 to the cybersecurity intelligence hub 206 and transmitting the object 216, in an unmodified state, to the second cybersecurity system 204. The second cybersecurity system 204 may then retrieve the first context information 218 from the cybersecurity intelligence hub 206. In one specific embodiment, the second cybersecurity system 204 may compute a hash of the object 216 and query the cybersecurity intelligence hub 206 for context information that includes the hash of the object 216 or may be referenced by content within the hash of the object 216. Additionally, the query to the cybersecurity intelligence hub 206 may include an identifier of the first cybersecurity system 202. The context information 216 may be stored in the cybersecurity intelligence hub 206 with the identifier indicating that the context information 216 was uploaded by the first cybersecurity system 202. In some cases, the header is only modified to provide information serving as an index into entries in the cybersecurity intelligence hub 206 where the context information 216 can be located.
In yet some embodiments, the object 216 and the first context information 218 may be uploaded to the cybersecurity intelligence hub 206 and a communication message transmitted to the second cybersecurity system 204 indicating that the object 216 and the context information 218 are available on the cybersecurity intelligence hub 206 for retrieval by the second cybersecurity system 204.
Although not illustrated, the malware analysis data sharing system 200 may include, or have access to, a rule (or model) database configured to store rules (or models) used in the malware analyses applied by the cybersecurity systems within the malware analysis data sharing system 200, e.g., the cybersecurity systems 202, 204 and/or 210. Each of the cybersecurity systems 202, 204 and/or 210 may be communicatively coupled to the database. In particular, the context information shared between cybersecurity systems, e.g., the context information 218, may include one or more rules, models, or a representation thereof, implicated during the malware analysis corresponding to the context information. Thus, the cybersecurity system that receives the context information, e.g., the second cybersecurity system 204 receiving the first context information 218, may query the database to determine the rules (or models) represented by the representation of the implicated rules within the context information 128. Although
Upon obtaining the context information 218, the second cybersecurity system 204 performs one or more operations to determine the origination of the object 216, e.g., the vector by which the object 216 entered the enterprise network, which, in this embodiment, is via the first cybersecurity system 202. Determination of the origination of the object 216 is advantageous for the second cybersecurity system 204 as the origination may be used to prioritize or adjust analysis of the object 216 performed by the second cybersecurity system 204.
A. Prioritization
A cybersecurity system may prioritize its analysis of an object by modifying its analysis to utilize an amended rule set, activate certain monitors of a cybersecurity system, and analyze the object according to the prioritized analysis based at least in part on received context information. In one embodiment, the prioritization of the analysis of an object may be determined, at least in part, by object type and origination according to a set of predetermined prioritization rules. The prioritization rules, which may be stored in a rules store, or stored with or communicatively coupled to the cybersecurity system, may set forth logic (e.g., a decision tree) that indicates the modified rule set to be used in analysis of the object 216 based on object type and origination (other factors may be considered as well, such as the verdict or score determined by the first cybersecurity system 202 and/or one or more rules implicated during the first malware analysis). In order to determine the origination of the object 216, the second cybersecurity system 204 parses the context information 218, which includes origination information of the object 216 and associated objects analyzed by the first cybersecurity system 202 along with a hash of each object. Upon determining the origination information, the second cybersecurity system 204 may prioritize the malware analysis of the object 216 as discussed above. Additionally, in some embodiments, the second cybersecurity system 204 may cause a query, which includes a hash of the object, to be transmitted to the cybersecurity intelligence hub 206, which may correlate the hash with stored hashes in order to rapidly reach a conclusion as to whether the object is known malware based on prior analyses and stored verdicts.
More specifically, the second cybersecurity system 204 may modify its monitoring and malware analysis of the object 216 based on the origination of the object 216. For example, assuming the object 216 includes an attached, embedded, or linked PDF object, the second cybersecurity system 204 may typically perform a malware analysis that includes applying a first predetermined rule set to detected behaviors, during or subsequent to, monitoring the processing of PDF objects; however, the origination of the object 216 may indicate that a second predetermined rule set (or second model) is to be used. For example, based on the origination of the object 216, a more stringent rule set (or model) may be applied during the monitoring and analysis of the object 216 than would otherwise be applied. Specific examples are provided below with respect to
Upon performing a modified monitoring and malware analysis, the second cybersecurity system 204 may perform a classification phase in which the results of the analysis are utilized by the network cybersecurity system to classify the network request (e.g., suspicious, malicious, benign, or indeterminate). The second cybersecurity system 204 may also prioritize its classification phase by modifying classification or correlation rules used, altering predetermined thresholds for determining maliciousness, altering weights associated with monitored activities, etc.
Further, upon performing a modified monitoring and malware analysis, the second cybersecurity system 204 generates second context information 222. Similar to the first context information 218, the second context information 222 may include origination information of each object analyzed by the second cybersecurity system 204, any verdict, any rules implicated during analysis, and a hash of each object analyzed. It should be noted that the second cybersecurity system 204 does not alter the origination information (e.g., the origination information is passed along from the first cybersecurity system 202 to the second cybersecurity system 204 and to the third cybersecurity system 210). The second context information 222 may also provide the verdict determined by the first cybersecurity system 202, one or more rules implicated by the malware analysis of the first cybersecurity system 202 as well as the verdict determined by the second cybersecurity system 204 and one or more rules implicated by the malware analysis of the second cybersecurity system 204.
As is illustrated in
Upon completion of the second malware analysis, a reporting logic of the second cybersecurity system 204, i.e., the reporting logic 836 of
IV. General Operations
Referring to
Upon completing its malware analysis, the first cybersecurity system provides at least the context information to a second cybersecurity system (block 304). As discussed above, providing the context information may be via active sharing by modifying the object under analysis to include the context information. However, in other embodiments, the first cybersecurity system need not provide the context information via active sharing but may instead provide the context information to a cybersecurity intelligence hub for retrieval by the second cybersecurity system.
Referring still to
Following the second malware analysis, the second cybersecurity system generates second context information and, optionally, issues a report based on the second malware analysis, which may include the second context information (block 308). The generation of the report is based indirectly, and may optionally be based directly, on the first context information generated by the first cybersecurity system as well as the second context information. Optionally, the second cybersecurity system may transmit at least the second context information to the cybersecurity intelligence hub, or to the management system (block 310).
IV. Exemplary System Architecture
Referring now to
In one embodiment, the email cybersecurity system 402 may include logic that analyzes electronic messages, e.g., emails, transmitted to and from the malware analysis data sharing system 400 to identify malicious content within emails. Analysis of an email may include a two-phase approach for detecting malware, i.e., a static phase and a dynamic phase. During the static phases, the email is inspected for protocol anomalies and other characteristics indicative of a cyberattack. During the dynamic phase, a virtual machine deployed within the cybersecurity system executes objects obtained from the network traffic being analyzed and monitors the behaviors of each object during execution.
As mentioned above, the endpoint 404 may include a cybersecurity system, namely an agent 404 operating as a daemon process on the endpoint 4031. The agent 404 monitors, captures, and, in some embodiments, analyzes the processing performed on the endpoint 4031 in real-time to identify indicators of compromise of the endpoint 4031 (that is, processing activity potentially indicative of a cyberattack).
In one embodiment, the endpoint 4031 on which the agent 404 is installed communicates over a network, which may be in common with other endpoints 4032-4031, with a STEM or other threat management system. The threat management system 407, may be included in the management system 408, and may be one dedicated to managing threats at endpoints 4031-403i, depending on the embodiment. The threat management system 407 in the endpoint-dedicated embodiment serves to aggregate the events, context information and, if made available, any verdicts reached by the supported endpoints 4031-403i, perform cyberattack detection (or validation, depending on whether the agents installed on each endpoint include cyberattack detection), and correlation across supported endpoints and classification, based on the provided information, issue alerts via a user interface, and, in some embodiments, initiate remedial action on the affected endpoints or issue recommended remedial action.
Further, the network cybersecurity system 410 may operate to intercept outbound network traffic including network requests to download webpages from a remote server over a public network (e.g., the internet), and analyze the intercepted traffic to determine whether the traffic is likely associated with malware, as well as in-bound responses to the requests including any of the webpages. Embodiments of the network cybersecurity system 410 may utilize a two-phase approach as discussed above. In particular, the email cybersecurity system 402, the agent 404 (operating on an endpoint) and the network cybersecurity system 410 may each apply different predetermined rule sets to their analyses. For example, the email cybersecurity system 402 may apply email-centric rules, such as rules directed to the analysis of attachments, hot-linked content, and embedded objects such as URLs for example, which may be more aggressive, e.g., stringent, in providing higher scores of maliciousness for URLs in or attachments to emails than other cybersecurity systems, e.g., network cybersecurity systems, due to the high number of malware attacks stemming from emails. Differently, network cybersecurity systems may apply rules associated with webpages as well as attachments and embedded or hyperlinked files such as WORD® documents or PDFs and hot-linked content. Further, agents may apply rules related to monitoring of typical applications running on the endpoint as well as executables including known malware to detect indicators of compromise, e.g., processing activities.
As is seen in the illustration of
Upon receipt of the email 416, the email cybersecurity system 402 performs a malware analysis on the email 416. In addition to performing a first malware analysis, the email cybersecurity system 402 generates email context information 418. The email context information 418 may include, but is not limited or restricted to, the type of malware analysis performed, a verdict determined as a result of the first malware analysis, one or more rules implicated during the first malware analysis, origination information, a hash of the email 416 and any additional objects attached to or embedded in the email 416, as well as rules implicated in analysis of the additional objects, and a verdict of the analysis of each of the additional objects.
The email cybersecurity system 402 determines a verdict as a result of the first malware analysis (suspicious, malicious, benign or indeterminate) or a score reflecting maliciousness. When the first malware analysis results in a verdict of malicious or a score above a predetermined threshold, the email cybersecurity system 402 may generate and issue a report (not shown) to the management system 408 and one or more other components of, or communicatively coupled to, the malware analysis data sharing system 400. The report may include a visual representation of the email context information 418.
When the first malware analysis results in a verdict other than malicious (suspicious, benign or indeterminate) or a score less than the predetermined threshold, the email cybersecurity system 402 provides the email context information 418 to the endpoint 4031 through either active sharing and/or passive sharing as discussed above. Additionally, the email cybersecurity system 402 may optionally provide the context information 408 to the cybersecurity intelligence hub 406 and/or the management system 408 for correlation with context information or other data received from cybersecurity systems. In some embodiments, the management system 408 may relay the email 416 and the context information 418 to the endpoint 4031.
In one embodiment as illustrated in
Alternatively, or in addition to active sharing, the endpoint 4031 may obtain the email context information 418 via passive sharing techniques. For example, the email cybersecurity system 402 may passively share the email context information 418 with the endpoint 4031 by uploading the email context information 418 to the cybersecurity intelligence hub 406 and transmitting the email 416, in an unmodified state, to the endpoint 4031. The agent 404 may then cause the endpoint 4031 to retrieve the email context information 418 from the cybersecurity intelligence hub 406. As discussed above, in one specific embodiment, the agent 404 may compute a hash of the email 416 and query the cybersecurity intelligence hub 406 for context information associated with the hash of the email 416. Additionally, the query to the cybersecurity intelligence hub 406 may include an identifier of the email cybersecurity system 402, which may be used by the cybersecurity intelligence hub 406 to provide the applicable context information as referenced above.
Upon obtaining the context information 418, the agent 404 performs one or more operations to determine the origination of the email 416, which may include parsing the context information 418 for an indicator of the origination (e.g., email, other network traffic, removable storage via an endpoint, etc.). As stated above, in the embodiment illustrated in
Specifically, due to the vast number of processes and objects for a cybersecurity system to analyze, in-depth analysis of every process and object is not typically viable as discussed above. In the embodiment illustrated in
Determining the origination of the email 416 may enable the agent 404 to prioritize the analysis of the processes and objects associated with the email 416. For example, processes initiated by an object associated with an email (e.g., an attachment) may receive a more scrutinized monitoring and result in, based on predetermined rules or a configuration of the agent 404 and responsive to the origination and other context information, a “suspicious” verdict for the email, a higher level of suspiciousness than processes initiated by other objects, or even a malicious verdict. Therefore, by determining which objects and processes originated via email (e.g., those associated with the email 416), the agent 404 is able to prioritize, e.g., tailor, the monitoring of processing activities of these objects and processes. As discussed above, the agent 404 may determine the origination from the context information 418.
As discussed above, prioritizing the analysis and/or monitoring of objects and processes based on origination information may result in a modification of one or more rule sets used in a malware analysis of the email 416, associated objects and corresponding processes to tailor the malware analysis thereof based on the origination. For example, assuming the email 416 includes a PDF object as an attachment, the agent 404 may typically perform a malware analysis that includes applying a first predetermined rule set (or model) to detected behaviors while monitoring the processing of PDF objects; however, the origination of the PDF attachment being via email may indicate that a second predetermined rule set is to be used, e.g., a more stringent rule set may be applied during the monitoring of the PDF attachment than would otherwise be applied.
Upon performing a modified malware analysis, the agent 404 generates second context information 420. Similar to the email context information 418, the second context information may include origination information of each object analyzed by the agent 404 along with a hash of each object analyzed as well as other information as described above. The second context information 420 may also provide the verdict determined by the agent 404, one or more rules implicated by the malware analysis of the agent 404 as well as the verdict determined by the email cybersecurity system 402 and one or more rules implicated by the malware analysis of the email cybersecurity system 402.
As is illustrated in
In response to the generation of the network request 422, in one embodiment wherein active sharing is implemented, a header logic of the agent 404, i.e., the header logic 922 as illustrated in
Upon receiving the modified network request 422, the network cybersecurity system 410 may determine the origination of the network request 422 based on the context information 420. The determination of the origination of the network request 422 may be done in a similar manner as discussed above with respect to the determination of the origination of the email 416 by the agent 404. For example, the network cybersecurity system 410 may parse the context information 420 included in the network request 422 for the origination information included therein. In this embodiment, the context information 420 may indicate that the network request 422 was generated as a result of processing of either the email 416, or an associated object, that entered the enterprise network via email. Thus, the network cybersecurity system 410 may prioritize the analysis, e.g., a third malware analysis, of the network request 422 accordingly, such as applying a modified rule set during analysis. As further illustrated in
Additionally, in some embodiments, the inbound response may include identifying information enabling the network cybersecurity system 410 to pair the inbound response with the outbound network request 422, and, thus utilize at least the context information 420 in analyzing the inbound response. Subsequent to, or concurrent with, the third malware analysis, as well as the analysis of the inbound response, the network cybersecurity system 410 may generate third context information 424 based on the third malware analysis and the analysis of the inbound response, if applicable.
Upon completion of the third malware analysis, the network cybersecurity system 410 may generate and issue a report 426 to one or more of the endpoint 4031. Additionally, the network cybersecurity system 410 may provide the report 426 and/or the third context information 424 to the cybersecurity intelligence hub 406 and/or the management system 408 (which may provide the report 426 and the third context information 424 to the administrator/cybersecurity specialist 412). It should also be noted that, although not shown, reports may be generated by the email cybersecurity system 402 and/or the agent 404. Such reports may be uploaded to the cybersecurity intelligence hub 406 and/or the management system 408 for storage.
When the third malware analysis results in a verdict indicating the network request 420 is associated with malware or a cyberattack, the network cybersecurity system 410 may facilitate a remediation process. In one embodiment, the network cybersecurity system 410 may include a retroactive detection logic discussed above. Alternatively, the network cybersecurity system 410 may provide the third context information 424 to the management system 408 and/or directly or indirectly to the agent 404, either of which may include a retroactive detection logic and facilitates remediation of any affected devices by tracing the path through which the email 416 or its associated objects traveled within the enterprise network and causing other endpoints along that path (and other endpoints to whom the email may have been forwarded) to block the opening and processing of the email and attachments thereto (if any) and further forwarding of the email by an email application. Operations of the retroactive detection logic are discussed above.
Referring now to
The email cybersecurity system analyzes the email object and the attached object, e.g., via static and/or dynamic analyses. Prior to, following, or concurrent with, the analyses, the email cybersecurity system generates context information including at least a hash of the email object, a hash of the attachment and embedded URLs, and a verdict of the analyses (block 504). However, as discussed above, the context information may include additional information. Following the generation of the context information, the email cybersecurity system modifies the email to include the context information (block 506). In one embodiment, the email cybersecurity system modifies the header of the email object by adding a X-header to the email header with the X-header containing the context information.
As an optional operation performed in the method 500, the email cybersecurity system may transmit the email and/or the context information to a management system and/or a cybersecurity intelligence hub (block 508). The transmission of the context information to the cybersecurity intelligence hub enables the passive sharing of the context information between the email cybersecurity system and the other cybersecurity systems within the enterprise network.
Upon modifying the email object to include the context information, the email cybersecurity system transmits the modified email to the endpoint on which an agent cybersecurity system is installed (block 510). As discussed above, the agent cybersecurity system, in response to a particular activity such as an attempt to open the email, performs a malware analysis based at least in part on the context information provided by the email cybersecurity system.
Referring now to
In response to receipt of the email object having a modified header, the agent obtains the context information from the modified header and parses the context information to determine the origination of the email object (block 604). Additionally, as discussed above with respect to
Following the determination of the origination of each object included within the email object (e.g., such as an attachment or an embedded object), the agent prioritizes the monitoring and analysis, and optionally, the classification phase, of the object based at least in part on the origination of the object as discussed above (block 606). Based on the prioritization monitoring and analysis, the agent monitors activities of the object (and associated objects) for indicators of compromise (IoCs) that may indicate an association with malware or a cyberattack (block 608). In addition, the agent generates second context information based on the monitoring and analysis performed by the agent. In particular, the monitoring and analysis may include detection of certain behaviors or operations, that based on the application of one or more rule sets, may indicate that a behavior or operation has a high likelihood of being an IoC. Optionally, the endpoint may transmit at least the second context information to the cybersecurity hub and/or the management system (block 609).
In some instances during processing, an object or process may initiate a network request, e.g., a request to access a website or retrieve social media content via an application installed on the endpoint. Upon the initiation of a network request associated with the email object (e.g., or any object included therein) such as activation of a URL, the agent modifies the header of the network request to include at least a representation of the context information generated by the agent (block 610). In one embodiment, modification of the header of the network request may compromise including an additional network header, for example, as described above. Following modification of the header of the network request, the endpoint transmits the network request to a network cybersecurity system (block 612).
Referring now to
Upon receiving the network request, the network cybersecurity system determines the origination of the network request based on the context information included in the modified header (block 704). With respect to network requests, context information may include information tracing the path of the network request through the malware analysis data sharing system as well as the origination of the initiating object. As one example, the context information of a network request may include an indication that the processing of an attachment of an email resulted in the initiation of the network request such that the origination is via email.
Based at least in part on the context information generated by the agent, the network cybersecurity system prioritizes the analysis of the network request, which may be performed by the scheduler 828 as illustrated in
Upon prioritizing the analysis of the network request, the network cybersecurity system analyzes the network request (or a copy thereof, depending on the deployment) from the endpoint according to the prioritized, e.g., tailored, analysis (block 708). When the network request is not found to be malicious (or in some situations, even when found to be malicious, in order to examine a response), the network cybersecurity system transmits the outbound network request to a public network, i.e., the internet (optional block 710). The network cybersecurity system may receive an inbound response, associate the inbound response with the outbound network request and analyze the inbound response based at least in part on the context information corresponding to the network request and generated by the agent (optional block 712). For example, in some embodiments, the inbound response may include identifying information enabling the network cybersecurity system to pair the inbound response with the outbound network request, and, thus utilize at least the context information corresponding to the network request in analyzing the inbound response.
Following the analysis of the network request (and optionally of the inbound response when applicable), the network cybersecurity system performs a correlation or classification phase in which at least the results of the analysis are utilized by the network cybersecurity system to classify the network request (e.g., suspicious, malicious, benign, or indeterminate) (block 714). In some embodiments, context information from prior analyses of the object that initiated the network request, including any classification verdicts, may be utilized in the classification phase of the network cybersecurity system. Upon completion of the correlation or classification phase, the network cybersecurity system generates third context information and issues a report based on the results of the analysis and the correlation or classification phase (block 716). Additionally, optionally, the network cybersecurity system may transmit at least the third context information to the cybersecurity hub and/or the management system (718). It should be understood that each cybersecurity system discussed in the disclosure may perform a correlation or classification phase upon completing a malware analysis.
Referring now to
The cybersecurity system appliance 800 may also include a persistent storage 808 that is communicatively coupled to the processors 802 and stores one or more executable software components configured to monitor behaviors and characteristics of the object 842. Specifically, the persistent storage 808 may include the following logic as software modules: a header logic 820, a (pre-and-post) traffic analysis logic 812, a rules engine 814, a header encoder/decoder logic 816, a rules store 818, a detection engine 820 including a static analysis engine 822, a dynamic analysis engine 824 and a suspiciousness determination logic 826, a scheduler 828 including prioritization logic 830 and a detection engine configurator 832, a classification logic 834, a reporting logic 836, a network interface logic 838 and a retroactive detection logic 840.
Numerous operations of the software modules, upon execution by the processor(s) 802, are described above and below. However, in some detail, the traffic analysis logic 812 is configured to perform pre- and post-analysis of communications, which may include parsing the communications (incoming or outgoing) and extracting the headers. For incoming communications, the traffic analysis logic 812 extracts the header, e.g., of object 842, and transmit the header to the header logic 810 which parses the header for the context information (the header logic 810 then passes the context information to the scheduler 828). For outgoing communications, e.g., a network request (not shown) detected during processing of the object 842 in the dynamic analysis engine 824, the traffic analysis logic 812 receives notice of such network request from the detection engine 820 and provides information to the header logic 810 to ensure that context information is added to the header of the network request. Additionally, a header encoder/decoder logic 816 is configured to (i) decode a header including context information upon receipt of an object, and (ii) encode a header with context information prior to transmission of an object. More specifically, the header encoder/decoder logic 816 receives an encoded header from the header logic 810 and decodes the header, e.g., decodes a X-header of a received email to enable the header logic 810 to parse the header for context information. In addition, the header encoder/decoder logic 816 receives a header of an object that has been modified by the header logic 810 and encodes the header prior to transmission of the object.
The static analysis engine 822 is configured to perform a static phase, during which network traffic is examined (without execution), for example, for signature-based matches against a library of known malware, and/or for communication-based protocol anomalies or other characteristics upon which a cybersecurity classification may be based. The dynamic analysis engine 824 is configured to perform a dynamic phase, during which a virtual machine deployed within the cybersecurity system executes objects obtained from the network traffic and monitors the behaviors of each object during execution where information regarding each monitored behavior may also be referred to as an “event” and may be used in cybersecurity classification of the object.
The suspiciousness determination logic 826 is configured as an intermediary logic engine between the static analysis engine 822 and the dynamic analysis engine 824 that modifies the scoring and weighting used by the static analysis engine 822 in determining suspiciousness or maliciousness of the object 824. The suspiciousness determination logic 826 may perform a series of operations. First, the suspiciousness determination logic 826 assigns one or more weights or scores to each of the observed characteristics of the static analysis in accordance with a set of rules (e.g., a default set of static analysis rules). Second, the suspiciousness determination logic 826 compares a combined score or weight of the observed characteristics against a first threshold, e.g., established by a second rule set, e.g., different than the static analysis rules. As a third operation, when the combined score is greater than or equal to the first threshold, the suspiciousness determination logic 826 passes the object 842 to the dynamic analysis engine 824. However, is some embodiments, the object 842 may be passed to the dynamic analysis engine 824 regardless of whether the combined score is greater than or equal to the first threshold. As a fourth operation, upon receiving a score or weight from the dynamic analysis engine 824, the suspiciousness determination logic 826 combines the dynamic analysis score or weight with the scores or weights of the static analysis and compares the total combined score or weight against a second threshold, which may be the same as or different than the first threshold before providing the results to the classification logic when the second threshold is met or exceeded. In some embodiments, the suspiciousness determination logic 826 may implement additional iterations of either the static analysis and/or the dynamic analysis, with the same or different rules utilized therein.
Further, the dynamic analysis engine 824 may modify the first and second thresholds based on the context information corresponding to the object 842. In addition, the dynamic analysis engine 824 may apply additional weighting to the weights or scores of either the static analysis and/or the dynamic analysis results. For example, the suspiciousness determination logic 826 receives the context information (or alternatively instructions from the scheduler 828) and determines a modification of the scoring and weighting used by the static analysis engine 822. Thus, the context information may influence the verdict of the static analysis of the object 842, which may determine whether the object 842 is processed by the dynamic analysis engine 824 (i.e., the scoring and weighting may be modified based on the origination of the object 842).
The prioritization logic 830, included within the scheduler 828, may be configured to utilize received context information to modify (i) the rule sets utilized by the detection engine 820 in the analysis of the object 842, (ii) the correlation rules used by the classification logic 834, (iii) the thresholds used by the classification logic 834, (iv) a time duration for processing the object 842 with the dynamic analysis engine 824, and/or (v) the scoring and weighting used by the classification logic 834. In addition, the detection engine configurator 832 may coordinate performance of a deeper static analysis and/or an augmented static analysis via a remote cybersecurity system based on the context information (and the results of the static analysis in some embodiments).
The detection engine configurator 832, which is present in network cybersecurity systems, may be configured to (i) schedule when the object 842 is to be processed and analyzed in light of the context information, (ii) determine that the object 842 will be processed with a specific guest image within a virtual machine due to the context information indicating one or more particular applications should be running in the dynamic analysis environment, and/or (iii) determine the object 842 will be monitored according to a specific instrumentation package (i.e., a set of process monitors) based on the context information. Of course, it is contemplated that some or all of this logic may be implemented as hardware, and if so, such logic could be implemented separately from each other.
Referring now to
The hardware processor 902 is a multipurpose, programmable device that accepts digital data as input, processes the input data according to instructions stored in its memory, and provides results as output. One example of the hardware processor 902 is an Intel® microprocessor with its associated instruction set architecture, which is used as a central processing unit (CPU) of the endpoint 900. Alternatively, the hardware processor 902 may include another type of CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), or the like.
The network device(s) 906 may include various input/output (I/O) or peripheral devices, such as a storage device, for example. One type of storage device may include a solid state drive (SSD) embodied as a flash storage device or other non-volatile, solid-state electronic device (e.g., drives based on storage class memory components). Another type of storage device may include a hard disk drive (HDD). Each network device 906 may include one or more network ports containing the mechanical, electrical and/or signaling circuitry needed to connect the endpoint 900 to a private network to thereby facilitate communications over a system network. To that end, the network interface(s) 904 may be configured to transmit and/or receive messages using a variety of communication protocols including, inter alia, TCP/IP and HTTPS.
The persistent storage 912 may include a plurality of locations that are addressable by the hardware processor 902 and the network interface(s) 904 for storing software (including software applications) and data structures associated with such software. The hardware processor 902 is adapted to manipulate the stored data structures as well as execute the stored software, which includes an operating system (OS) 914, one or more applications 916 and the agent 918.
The operating system (OS) 914 is software that manages hardware, software resources, and provides common services for computer programs, such as applications 916. For hardware functions such as input and output (I/O) and memory allocation, the operating system 914 acts as an intermediary between applications 916 and the computer hardware, although the application code is usually executed directly by the hardware and frequently makes system calls to an OS function or be interrupted by it.
The agent 918 is comprised of one or more executable software components configured to monitor activities of the applications 916 and/or operating system 914. Specifically, the agent 918 may be comprised of the following logic as software modules: one or more process monitors 902, a header logic 922, interprocess interface logic 924, header encoder/decoder logic 926, classification logic 928, reporting logic 930, detection engine 932, detection engine configurator 934, rules store 936, and retroactive detection logic 938.
Numerous operations of the software modules, upon execution by the processor(s) 902, are described above. However, in some detail, the one or more process monitors 902 are configured to monitor the processing activity that is occurring on the endpoint 900. In particular, rules, stored in the rules stored 936 are used to assist the agent 918 in activities that are to be monitored, as well as in detection and classification of the activities. The rules store 936 is also configured to store prioritization rules, classification rules, etc., as discussed above. The rules store 936 may be, periodically or aperiodically, updated to reflect additional rule sets, remove obsolete rules, etc. In some embodiments, the agent 918 may perform classification via the classification logic 928, e.g., using correlation rules stored in the rules store 936. In other embodiments, the agent 918 may monitor activities and log certain activities to be reported to a separate network device such as a threat management system or a SIEMs for correlation and classification.
The header logic 922 is configured to parse a header of a received object (i.e., the object 905), such as an email or a network request. As discussed above, in the case of an email object, the header logic 922 parses the email header for a X-header including context information. In addition, as discussed above, upon generating context information, the header logic 922 may modify the header of the object 905 to include the generated context information prior to the endpoint 900 transmitting the object 905 to another cybersecurity system and/or another endpoint. Additionally, when a second object, e.g., a network request associated with the object 905, is to be transmitted to a network cybersecurity system, the header logic 922 modifies the header of the network request to include context information generated according to the analysis of the object 905 by the agent 918. In the case of a network request, the header logic 922 modifies a HTTP header and/or adds a network header in order to include the context information.
The interprocess interface logic 924 is configured to enable interaction and cooperation between the agent 918 and processes that are running on the endpoint 900 to enable the agent 918 to receive information when events occur (e.g., when the endpoint 900 receives an email). The header encoder/decoder logic 926 is configured to (i) decode a header including context information upon receipt of an object, and (ii) encode a header with context information prior to transmission of an object. More specifically, the header encoder/decoder logic 926 receives an encoded header from the header logic 922 and decodes the header, e.g., decodes a X-header of a received email to enable the header logic 922 to parse the header for context information. In addition, the header encoder/decoder logic 926 receives a header of an object that has been modified by the header logic 922 and encodes the header prior to transmission of the object.
The detection engine 932, in the agent 900, activates process monitors for the analysis of the object 905 and detects specific activities that may indicate a likelihood of an association with a cyberattack. The classification logic 928 is configured to perform a classification phase, using correlation rules, in which the results of the analysis by the agent 918 are utilized to classify the object 905 (e.g., suspicious, malicious, benign, or indeterminate).
The detection engine configurator 934 is configured to determine one or more rule sets to be employed by the detection engine 932 and the classification logic 928. As discussed above, the prioritization of an agent's monitoring and analysis may result in a tailoring of the typical rule sets used by the agent based on the context information corresponding to the object 905 by the endpoint 900. The reporting logic 930 may be configured to generate and issue (or cause the endpoint 900, STEM or an endpoint threat management system with which it communicates to issue) a report.
The retroactive detection logic 938 may be optionally included in the agent 918 or may alternatively be included in a threat management system. The retroactive detection logic 938 is configured to facilitate remediation of an affected device (e.g., endpoint 900). The retroactive detection logic 938 may utilize context information to trace the path through which a malicious object traveled within the enterprise network. By determining the path through which the malicious object traveled, the retroactive detection logic 938 can initiate remediation efforts on any affected network device (e.g., quarantine the device on the network and/or the object within the device). Of course, it is contemplated that some or all of this logic may be implemented as hardware, and if so, such logic could be implemented separately from each other.
In the foregoing description, the invention is described with reference to specific exemplary embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention as set forth in the appended claims. For example, in lieu of the use of rules-based cybersecurity systems, as described above, model based cybersecurity systems may be deployed in which models are used to conduct the malware analysis in lieu of a rule set as identified throughout portions of the specification. While rules are applied in series to incoming data to produce a result, for models, the entire model is applied to incoming data and a result is produced therefrom. This disclosure discusses the use of rules for purposes of clarity; however, the disclosure is not intended to be so limiting as any analysis control scheme may be utilized.
This application is a continuation of U.S. patent application Ser. No. 16/353,982, issued as U.S. Pat. No. 11,558,401, which is based upon and claims the benefit of priority from U.S. Provisional Patent Application No. 62/650,946 filed Mar. 30, 2018, the entire contents of both of which are incorporated herein by reference.
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| Number | Date | Country | |
|---|---|---|---|
| 62650946 | Mar 2018 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16353982 | Mar 2019 | US |
| Child | 18097091 | US |
