Enterprise computer networks often utilize data loss prevention (DLP) software that contains policies for protecting sensitive data from malicious users and/or preventing sensitive data leaks. Traditional DLP solutions may include content policies for monitoring outbound network files, identifying file types, extracting file content, running a DLP policy based on the content, and taking any necessary corrective action (such as blocking the downloading of the network file) based on the policy.
Unfortunately, traditional DLP solutions often suffer from a number of drawbacks that may prevent them from detecting sensitive outbound operations and/or the exfiltration (e.g., data theft) of sensitive content. For example, traditional DLP solutions may fail to detect sensitive file types that a malicious user may modify to another file type prior to downloading to a removable storage device. Thus, a malicious user may use a hex editor to change a file type for sensitive files (e.g., a word processor file type) to a non-sensitive file type (e.g., an image file type) to avoid detection. Traditional DLP solutions may also fail to detect malicious software introduced to an enterprise network from an outside source (e.g., a USB drive) that may utilize a custom protocol to scan and transfer sensitive network files to an anonymous server.
As will be described in greater detail below, the instant disclosure describes various systems and methods for utilizing an information trail to enforce data loss prevention policies on potentially malicious file activity.
In one example, a computer-implemented method for utilizing lifecycle analytics to enforce data loss prevention policies on potentially malicious content may include (1) recording, by a computing device, one or more current activities associated with a file retrieved from a server, (2) linking, by the computing device, the current activities to one or more previously recorded activities associated with the file, (3) generating, by the computing device, a graph including nodes representing an information trail of related events associated with the current activities and the previously recorded activities, (4) determining, by the computing device, a severity of the information trail based on one or more rules, the severity associated with a likelihood of potential malicious activity, and (5) performing, by the computing device, a data loss prevention action on one or more operations associated with the file based on the potential malicious activity.
In some examples, the recording of current activities associated with a file retrieved from a server may include recording (1) a file creation operation, (2) a file copy operation, (3) a file delete operation, (4) a file read operation, (5) a file rename operation, (6) a file write operation, (7) file download operation, and/or (8) a file upload operation. In some examples, determining the severity of the information trail based on one or more rules may include: (1) identifying a file operation associated with each node in the information trail, (2) applying the one or more rules to the file operation, and (3) assigning a risk indicator to each node based on the one or more rules. In one example, the risk indicator may correspond to a likelihood of the potential malicious activity.
In some examples, the rules may include: (1) a content sensitivity associated with the file, (2) a mismatched file extension associated with the file, (3) a reputation of a process for accessing the file, (4) a blacklisted internet protocol address associated with the file, (5) a file encryption associated with the file, (6) exfiltration activity associated with the file, and/or (7) an endpoint location associated with the file.
In one example, the data loss prevention action may include includes blocking the operations associated with the file. Additionally, or alternatively, the data loss prevention action may include collecting data generated by the operations associated with the file for analysis. Additionally, or alternatively, the data loss prevention action may include collecting data generated by the operations for updating a data loss prevention model.
In one embodiment, a system for implementing the above-described method may include (1) a recording module, stored in memory, that records, by a computing device, one or more current activities associated with a file retrieved from a server, (2) a linking module, stored in memory, that links, by the computing device, the current activities to one or more previously recorded activities associated with the file, (3) a generation module, stored in memory, that generates, by the computing device, a graph including nodes representing an information trail of related events associated with the current activities and the previously recorded activities, (4) a determination module, stored in memory, that determines, by the computing device, a severity of the information trail based on one or more rules, the severity associated with a likelihood of potential malicious activity, (5) a security module, stored in memory, that performs, by the computing device, a data loss prevention action on one or more operations associated with the file based on the potential malicious activity, and (6) at least one physical processor configured to execute the recording module, the linking module, the generation module, the determination module, and the security module.
In some examples, the above-described method may be encoded as computer-readable instructions on a non-transitory computer-readable medium. For example, a computer-readable medium may include one or more computer-executable instructions that, when executed by at least one processor of a computing device, may cause the computing device to (1) record one or more current activities associated with a file retrieved from a server, (2) link the current activities to one or more previously recorded activities associated with the file, (3) generate a graph including nodes representing an information trail of related events associated with the current activities and the previously recorded activities, (4) determine a severity of the information trail based on one or more rules, the severity associated with a likelihood of potential malicious activity, and (5) perform a data loss prevention action on one or more operations associated with the file based on the potential malicious activity.
Features from any of the above-mentioned embodiments may be used in combination with one another in accordance with the general principles described herein. These and other embodiments, features, and advantages will be more fully understood upon reading the following detailed description in conjunction with the accompanying drawings and claims.
The accompanying drawings illustrate a number of example embodiments and are a part of the specification. Together with the following description, these drawings demonstrate and explain various principles of the instant disclosure.
Throughout the drawings, identical reference characters and descriptions indicate similar, but not necessarily identical, elements. While the example embodiments described herein are susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and will be described in detail herein. However, the example embodiments described herein are not intended to be limited to the particular forms disclosed. Rather, the instant disclosure covers all modifications, equivalents, and alternatives falling within the scope of the appended claims.
The present disclosure is generally directed to systems and methods for utilizing an information trail to enforce data loss prevention policies on potentially malicious file activity. As will be explained in greater detail below, by recording and correlating related activities and events such as file launching, file reading, and file creation activities on an endpoint device in a computing network, the systems and methods described herein may be able to create a visual information trail that identifies potentially risky file operations associated with malicious activity. By utilizing the information trail in this way, the systems and methods described herein may be able to improve the identification of malicious activity for applying data loss prevention (DLP) policies, thereby reducing leaks of sensitive content from a computer network when compared to traditional DLP software agents that only analyze individual file operations without correlating related events.
Moreover, the systems and methods described herein may improve the functioning and/or performance of an endpoint computing device in a computing network by detecting potentially malicious files with increased accuracy and thus reducing the likelihood of infection. These systems and methods may also improve the field of enterprise-level computer network security by detecting potentially malicious activities performed by file operations on endpoint devices, thereby protecting the computing network from malicious attacks.
The following will provide, with reference to
In certain embodiments, one or more of modules 102 in
As illustrated in
As illustrated in
As illustrated in
The term “information trail,” as used herein, generally refers to a graph of previous and current file activities that may be visually represented as a series of related events. In some examples, the graph may be composed of multiple nodes where each node represents a file. In some examples, the graph may include a node representing a primary file (e.g., an executable file) that may perform various operations (e.g., read, create, or launch operations) on other nodes representing additional files. In some examples, the graph may be utilized for determining potentially malicious activity associated with a primary file node based on the various operations performed with respect to the other file nodes.
The term “severity,” as used herein, generally refers to a likelihood of various file operations corresponding to malicious activity. In some examples, severity may be determined based on a series of rules defining potentially malicious file activities or attributes such as content sensitivity, a file type not matching a file extension, reputation of a process for accessing a file, a blacklisted IP address as a file exfiltration destination, encrypted (e.g., uncrackable) files, an exfiltration mode or device associated with a file, and/or an endpoint location associated with a file.
The term “malicious activity,” as used herein, generally refers to any unauthorized activity associated with one or more files in violation of a data loss prevention policy. In some examples, malicious activity may include data theft (e.g., the exfiltration of files and/or data from a server or endpoint device), file modification, and/or the introduction of malicious files into a computing network.
Example system 100 in
Computing device 202 generally represents any type or form of computing device capable of reading computer-executable instructions. In one example, computing device 202 may represent an endpoint computing device running client-side DLP agent software in an enterprise computing network. Additional examples of computing device 202 include, without limitation, laptops, tablets, desktops, servers, cellular phones, Personal Digital Assistants (PDAs), multimedia players, embedded systems, wearable devices (e.g., smart watches, smart glasses, etc.), smart vehicles, smart packaging (e.g., active or intelligent packaging), gaming consoles, so-called Internet-of-Things devices (e.g., smart appliances, etc.), variations or combinations of one or more of the same, and/or any other suitable computing device.
Server 206 generally represents any type or form of computing device that is capable of hosting files 208 and data loss prevention policies 220. In one example, server 206 may be a DLP server for storing files 208, in accordance with data loss prevention policies 220, in an enterprise computing network. Additional examples of server 206 include, without limitation, security servers, application servers, web servers, storage servers, and/or database servers configured to run certain software applications and/or provide various security, web, storage, and/or database services. Although illustrated as a single entity in
Network 204 generally represents any medium or architecture capable of facilitating communication or data transfer. In one example, network 204 may facilitate communication between computing device 202 and server 206. In this example, network 204 may facilitate communication or data transfer using wireless and/or wired connections. Examples of network 204 include, without limitation, an intranet, a Wide Area Network (WAN), a Local Area Network (LAN), a Personal Area Network (PAN), the Internet, Power Line Communications (PLC), a cellular network (e.g., a Global System for Mobile Communications (GSM) network), portions of one or more of the same, variations or combinations of one or more of the same, and/or any other suitable network.
As illustrated in
Recording module 104 may record current file activities 122 in a variety of ways. In one example, recording module 104 may be a component of a client-side DLP agent that monitors operations associated with files 208 retrieved from server 206 in accordance with data loss prevention policies 220. In some examples, monitored file operations may include, without limitation, file creation operations, file copy operations, file delete operations, file read operations, file rename operations, file write operations, file download operations, and/or file upload operations.
At step 304 in
Linking module 106 may link current file activities 122 with previously recorded file activities 124 in a variety of ways. In one example, linking module 106 may be a component of a client-side DLP agent that associates previous operations performed by a file 208 with current operations. For example, linking module 106 may associate a previously recorded file read operation of a spreadsheet file with a current file creation operation associated with a generic data file.
At step 306 in
Generation module 108 may generate information trail 126 in a variety of ways. In one example, generation module 108 may be a component of a client-side DLP agent that generates information trail 126 as a graph of file nodes showing related events associated with linked current file activities 122 and previously recorded file activities 124. An example information trail 126 is shown in
As shown in
As will be discussed in greater detail below, the related events represented by the file operations associated with file node 404 may be analyzed to determine potential malicious activity. For example, the change of a spreadsheet file extension associated with the file node 406 to a PDF file extension associated with file node 408 may indicate an attempt by a malicious user to obfuscate the theft of sensitive spreadsheet content disclosing a company's quarterly results as a press article in order to avoid detection by a company's DLP software.
Returning now to
Determination module 110 may determine the severity of information trail 126 based on rules 126 in a variety of ways. In one example, determination module 110 may be a component of a client-side DLP agent that determines the severity of information trail 126 by identifying a file operation associated with each node in information trail 126, apply rules 128 to the file operation, and assign a risk indicator to each node based on applied rules 128. In some examples, the risk indicator may correspond to a likelihood of potential malicious activity associated with a file 208. In some examples, rules 128 may include a content sensitivity, a mismatched file extension, a reputation of a process for accessing a file 208, a blacklisted IP address associated with a file 208, a file encryption, exfiltration activity, and/or an endpoint location associated with a file 208. As an example, and as discussed above with respect to
At step 310 in
Security module 112 may perform a data loss prevention action 210 in a variety of ways. In one example security module 112 may be a component of a client-side DLP agent that may block on one or more outbound operations associated with a file 208. For example, security module 112 may block a current read or write operation involving a file 208 with a changed file extension, prevent a save operation for a file 208 to a removable media (e.g., a USB drive) that has previously been accessed by a process having a low reputation, and/or prevent a file 208 from being communicated over network 204 to a blacklisted IP address. Additionally, or alternatively, security module 112 may collect data generated by the one or more operations associated with a file 208 for analysis. For example, security module 112 may capture telemetry data associated with one or more potentially malicious information trails 126 and extract a malicious activity “signature.” For example, a malicious activity signature may include one or more operations associated with changing the file extension of a file 208 containing sensitive content or determining that a file 208 has been associated with a blacklisted IP address utilized by a malicious server. The extracted signature could then be distributed to other DLP agents for proactively detecting malicious activity. In some examples, multiple information trails 126 may be compared to detect common exfiltration patterns and understand user behavior. Additionally, or alternatively, security module 112 may collect data generated by the one or more file operations for updating a data loss prevention model. For example, security module 112 may capture telemetry data associated with one or more potentially malicious information trails 126 to supplement a data loss prevention model used to conduct a post mortem analysis following a data breach.
In the data flow diagram 500, a user 502 may generate one or more events that are received by an event monitoring service 504. An event aggregator 506 may aggregate the events from the event monitoring service 502 and send the events to a trail engine 506. Trail engine 506 may generate an information trail (such as information trail 126 of
In the data flow diagram 502, user 502 may also generate data exfiltration activity that may be received by connector 516 and sent to a detection/trail policy evaluation module 518. Detection/trail policy evaluation module 518 may also receive trail information from trail engine 508. Rule execution module 520 may execute rules (such as rules 128 of
As explained above in connection with
Computing system 610 broadly represents any single or multi-processor computing device or system capable of executing computer-readable instructions. Examples of computing system 610 include, without limitation, workstations, laptops, client-side terminals, servers, distributed computing systems, handheld devices, or any other computing system or device. In its most basic configuration, computing system 610 may include at least one processor 614 and a system memory 616.
Processor 614 generally represents any type or form of physical processing unit (e.g., a hardware-implemented central processing unit) capable of processing data or interpreting and executing instructions. In certain embodiments, processor 614 may receive instructions from a software application or module. These instructions may cause processor 614 to perform the functions of one or more of the example embodiments described and/or illustrated herein.
System memory 616 generally represents any type or form of volatile or non-volatile storage device or medium capable of storing data and/or other computer-readable instructions. Examples of system memory 616 include, without limitation, Random Access Memory (RAM), Read Only Memory (ROM), flash memory, or any other suitable memory device. Although not required, in certain embodiments computing system 610 may include both a volatile memory unit (such as, for example, system memory 616) and a non-volatile storage device (such as, for example, primary storage device 632, as described in detail below). In one example, one or more of modules 102 from
In some examples, system memory 616 may store and/or load an operating system 640 for execution by processor 614. In one example, operating system 640 may include and/or represent software that manages computer hardware and software resources and/or provides common services to computer programs and/or applications on computing system 610. Examples of operating system 640 include, without limitation, LINUX, JUNOS, MICROSOFT WINDOWS, WINDOWS MOBILE, MAC OS, APPLE'S 10S, UNIX, GOOGLE CHROME OS, GOOGLE'S ANDROID, SOLARIS, variations of one or more of the same, and/or any other suitable operating system.
In certain embodiments, example computing system 610 may also include one or more components or elements in addition to processor 614 and system memory 616. For example, as illustrated in
Memory controller 618 generally represents any type or form of device capable of handling memory or data or controlling communication between one or more components of computing system 610. For example, in certain embodiments memory controller 618 may control communication between processor 614, system memory 616, and I/O controller 620 via communication infrastructure 612.
I/O controller 620 generally represents any type or form of module capable of coordinating and/or controlling the input and output functions of a computing device. For example, in certain embodiments I/O controller 620 may control or facilitate transfer of data between one or more elements of computing system 610, such as processor 614, system memory 616, communication interface 622, display adapter 626, input interface 630, and storage interface 634.
As illustrated in
As illustrated in
Additionally or alternatively, example computing system 610 may include additional I/O devices. For example, example computing system 610 may include I/O device 636. In this example, I/O device 636 may include and/or represent a user interface that facilitates human interaction with computing system 610. Examples of I/O device 636 include, without limitation, a computer mouse, a keyboard, a monitor, a printer, a modem, a camera, a scanner, a microphone, a touchscreen device, variations or combinations of one or more of the same, and/or any other I/O device.
Communication interface 622 broadly represents any type or form of communication device or adapter capable of facilitating communication between example computing system 610 and one or more additional devices. For example, in certain embodiments communication interface 622 may facilitate communication between computing system 610 and a private or public network including additional computing systems. Examples of communication interface 622 include, without limitation, a wired network interface (such as a network interface card), a wireless network interface (such as a wireless network interface card), a modem, and any other suitable interface. In at least one embodiment, communication interface 622 may provide a direct connection to a remote server via a direct link to a network, such as the Internet. Communication interface 622 may also indirectly provide such a connection through, for example, a local area network (such as an Ethernet network), a personal area network, a telephone or cable network, a cellular telephone connection, a satellite data connection, or any other suitable connection.
In certain embodiments, communication interface 622 may also represent a host adapter configured to facilitate communication between computing system 610 and one or more additional network or storage devices via an external bus or communications channel. Examples of host adapters include, without limitation, Small Computer System Interface (SCSI) host adapters, Universal Serial Bus (USB) host adapters, Institute of Electrical and Electronics Engineers (IEEE) 1394 host adapters, Advanced Technology Attachment (ATA), Parallel ATA (PATA), Serial ATA (SATA), and External SATA (eSATA) host adapters, Fibre Channel interface adapters, Ethernet adapters, or the like. Communication interface 622 may also allow computing system 610 to engage in distributed or remote computing. For example, communication interface 622 may receive instructions from a remote device or send instructions to a remote device for execution.
In some examples, system memory 616 may store and/or load a network communication program 638 for execution by processor 614. In one example, network communication program 638 may include and/or represent software that enables computing system 610 to establish a network connection 642 with another computing system (not illustrated in
Although not illustrated in this way in
As illustrated in
In certain embodiments, storage devices 632 and 633 may be configured to read from and/or write to a removable storage unit configured to store computer software, data, or other computer-readable information. Examples of suitable removable storage units include, without limitation, a floppy disk, a magnetic tape, an optical disk, a flash memory device, or the like. Storage devices 632 and 633 may also include other similar structures or devices for allowing computer software, data, or other computer-readable instructions to be loaded into computing system 610. For example, storage devices 632 and 633 may be configured to read and write software, data, or other computer-readable information. Storage devices 632 and 633 may also be a part of computing system 610 or may be a separate device accessed through other interface systems.
Many other devices or subsystems may be connected to computing system 610. Conversely, all of the components and devices illustrated in
The computer-readable medium containing the computer program may be loaded into computing system 610. All or a portion of the computer program stored on the computer-readable medium may then be stored in system memory 616 and/or various portions of storage devices 632 and 633. When executed by processor 614, a computer program loaded into computing system 610 may cause processor 614 to perform and/or be a means for performing the functions of one or more of the example embodiments described and/or illustrated herein. Additionally or alternatively, one or more of the example embodiments described and/or illustrated herein may be implemented in firmware and/or hardware. For example, computing system 610 may be configured as an Application Specific Integrated Circuit (ASIC) adapted to implement one or more of the example embodiments disclosed herein.
Client systems 710, 720, and 730 generally represent any type or form of computing device or system, such as example computing system 610 in
As illustrated in
Servers 740 and 745 may also be connected to a Storage Area Network (SAN) fabric 780. SAN fabric 780 generally represents any type or form of computer network or architecture capable of facilitating communication between a plurality of storage devices. SAN fabric 780 may facilitate communication between servers 740 and 745 and a plurality of storage devices 790(1)-(N) and/or an intelligent storage array 795. SAN fabric 780 may also facilitate, via network 750 and servers 740 and 745, communication between client systems 710, 720, and 730 and storage devices 790(1)-(N) and/or intelligent storage array 795 in such a manner that devices 790(1)-(N) and array 795 appear as locally attached devices to client systems 710, 720, and 730. As with storage devices 760(1)-(N) and storage devices 770(1)-(N), storage devices 790(1)-(N) and intelligent storage array 795 generally represent any type or form of storage device or medium capable of storing data and/or other computer-readable instructions.
In certain embodiments, and with reference to example computing system 610 of
In at least one embodiment, all or a portion of one or more of the example embodiments disclosed herein may be encoded as a computer program and loaded onto and executed by server 740, server 745, storage devices 760(1)-(N), storage devices 770(1)-(N), storage devices 790(1)-(N), intelligent storage array 795, or any combination thereof. All or a portion of one or more of the example embodiments disclosed herein may also be encoded as a computer program, stored in server 740, run by server 745, and distributed to client systems 710, 720, and 730 over network 750.
As detailed above, computing system 610 and/or one or more components of network architecture 700 may perform and/or be a means for performing, either alone or in combination with other elements, one or more steps of an example method for utilizing an information trail to enforce data loss prevention policies on potentially malicious file activity.
While the foregoing disclosure sets forth various embodiments using specific block diagrams, flowcharts, and examples, each block diagram component, flowchart step, operation, and/or component described and/or illustrated herein may be implemented, individually and/or collectively, using a wide range of hardware, software, or firmware (or any combination thereof) configurations. In addition, any disclosure of components contained within other components should be considered example in nature since many other architectures can be implemented to achieve the same functionality.
In some examples, all or a portion of example system 100 in
In various embodiments, all or a portion of example system 100 in
According to various embodiments, all or a portion of example system 100 in
In some examples, all or a portion of example system 100 in
In addition, all or a portion of example system 100 in
In some embodiments, all or a portion of example system 100 in
According to some examples, all or a portion of example system 100 in
The process parameters and sequence of steps described and/or illustrated herein are given by way of example only and can be varied as desired. For example, while the steps illustrated and/or described herein may be shown or discussed in a particular order, these steps do not necessarily need to be performed in the order illustrated or discussed. The various example methods described and/or illustrated herein may also omit one or more of the steps described or illustrated herein or include additional steps in addition to those disclosed.
While various embodiments have been described and/or illustrated herein in the context of fully functional computing systems, one or more of these example embodiments may be distributed as a program product in a variety of forms, regardless of the particular type of computer-readable media used to actually carry out the distribution. The embodiments disclosed herein may also be implemented using software modules that perform certain tasks. These software modules may include script, batch, or other executable files that may be stored on a computer-readable storage medium or in a computing system. In some embodiments, these software modules may configure a computing system to perform one or more of the example embodiments disclosed herein.
In addition, one or more of the modules described herein may transform data, physical devices, and/or representations of physical devices from one form to another. Additionally or alternatively, one or more of the modules recited herein may transform a processor, volatile memory, non-volatile memory, and/or any other portion of a physical computing device from one form to another by executing on the computing device, storing data on the computing device, and/or otherwise interacting with the computing device.
The preceding description has been provided to enable others skilled in the art to best utilize various aspects of the example embodiments disclosed herein. This example description is not intended to be exhaustive or to be limited to any precise form disclosed. Many modifications and variations are possible without departing from the spirit and scope of the instant disclosure. The embodiments disclosed herein should be considered in all respects illustrative and not restrictive. Reference should be made to the appended claims and their equivalents in determining the scope of the instant disclosure.
Unless otherwise noted, the terms “connected to” and “coupled to” (and their derivatives), as used in the specification and claims, are to be construed as permitting both direct and indirect (i.e., via other elements or components) connection. In addition, the terms “a” or “an,” as used in the specification and claims, are to be construed as meaning “at least one of.” Finally, for ease of use, the terms “including” and “having” (and their derivatives), as used in the specification and claims, are interchangeable with and have the same meaning as the word “comprising.”
Number | Date | Country | Kind |
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201811013062 | Apr 2018 | IN | national |