System and method for cybersecurity threat detection utilizing static and runtime data

Information

  • Patent Grant
  • 12278825
  • Patent Number
    12,278,825
  • Date Filed
    Monday, August 28, 2023
    a year ago
  • Date Issued
    Tuesday, April 15, 2025
    15 days ago
Abstract
A system and method for improved endpoint detection and response (EDR) in a cloud computing environment initiates inspection based on data received from a sensor deployed on a workload. The method includes: configuring a resource, deployed in a cloud computing environment, to deploy thereon a sensor, the sensor configured to detect runtime data; detecting a potential cybersecurity threat on the resource based on detected runtime data received from the sensor; and initiating inspection of the resource for the potential cybersecurity threat.
Description
TECHNICAL FIELD

The present disclosure relates generally to detection of cybersecurity threats, and specifically to complementary solutions for cybersecurity threat detection utilizing static analysis and runtime data.


BACKGROUND

Cybersecurity threats come in many shapes and forms, such as malware, worms, cryptominers, man-in-the-middle attacks, code injection, misconfigurations, and so on. Different threats pose different risks, and can often be detected in different ways. As such, there are many solutions which detect different types of cybersecurity threats, each with advantages and disadvantages. Cloud computing platforms, such as provided by Amazon® Web Services (AWS), Google® Cloud Platform (GCP), Microsoft® Azure, and the like, are high value targets for attackers, and therefore their vulnerabilities are more likely to become cybersecurity threats. It is therefore extremely useful to detect such cybersecurity threats.


For example, agent based solutions are able to detect both runtime and stored data, allowing to form a complete picture of the cybersecurity status of a machine having the agent installed thereon. However, agent based solutions require heavy use of compute resources, such as processor and memory resources. This is due to the agent being deployed on the machine which is scanned. For endpoints in a network, this type of solution is impractical, as the use of those resources is reserved for performing the task of the endpoint machine. Furthermore, some agent solutions also require communication with a backend which provides definitions, rules, and the like, in order to enable the agent to scan for cybersecurity threats using up to date information. Additionally, some agent based solutions require root privileges, or are deployed as a privileged software container. This in itself is a security risk, as conveying such permissions is inherently risky. Therefore, as an endpoint detection and response (EDR) solution for a cloud computing production environment, agent based solutions fail at their objective, and indeed such solutions are rarely used on network endpoints due to the above mentioned reasons.


Agentless solutions, on the other hand, do not require an agent installed on a machine. These solutions include static analysis, for example of a disk of a machine, to determine what cybersecurity threats are present. However, such solutions likewise fail at providing a complete picture, since static analysis solutions do not have access to runtime data. Such agentless solutions also fail to provide real time threat detection, thereby potentially leaving cybersecurity threats with a response for prolonged periods of time.


Utilizing both types of solution is not practical, as there is overlap in the data of agent and agentless solutions, and the computational costs of deploying both solutions on a single network are great. This leads, in practice, to a choice between either type of solution, with the resignation that some threats will inevitably go undetected.


It would therefore be advantageous to provide a solution that would overcome the challenges noted above.


SUMMARY

A summary of several example embodiments of the disclosure follows. This summary is provided for the convenience of the reader to provide a basic understanding of such embodiments and does not wholly define the breadth of the disclosure. This summary is not an extensive overview of all contemplated embodiments, and is intended to neither identify key or critical elements of all embodiments nor to delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more embodiments in a simplified form as a prelude to the more detailed description that is presented later. For convenience, the term “some embodiments” or “certain embodiments” may be used herein to refer to a single embodiment or multiple embodiments of the disclosure.


A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.


In one general aspect, method may include configuring a resource, deployed in a cloud computing environment, to deploy thereon a sensor, the sensor configured to detect runtime data. Method may also include detecting a potential cybersecurity threat on the resource based on detected runtime data received from the sensor. Method may furthermore include initiating inspection of the resource for the potential cybersecurity threat. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.


Implementations may include one or more of the following features. Method may include: detecting a disk associated with the resource; generating an inspectable disk based on the detected disk; and inspecting the inspectable disk for a cybersecurity object indicating the potential cybersecurity threat. Method may include: cloning the detected disk into the inspectable disk. Method may include: determining that the potential cybersecurity threat is an actual cybersecurity threat; and initiating a mitigation action based on the actual cybersecurity threat. Method may include: applying a logical expression of a definition to an event detected by the sensor; and determining that the potential cybersecurity threat is an actual cybersecurity threat in response to a binary outcome of the applied logical expression having a predetermined value. Method where the resource is a virtual machine, and the sensor is a service deployed on an operating system of the virtual machine. Method where the resource is a serverless function, and the sensor is a code layer of the serverless function, the serverless function further including a function code. Method where the resource is a software container, may include: configuring a container cluster of the software container to deploy a daemonset, the daemonset including a plurality of nodes, each node including a daemonset pod, where the daemonset pod is the deployed sensor. Method may include: sending a rule to the sensor, the rule including a logical expression and an action; configuring the sensor to apply the rule on a detected event; and configuring the sensor to perform the action in response to applying the rule on the detected event and receiving a predetermined result. Method may include: sending data pertaining to the detected event to a sensor backend server. Implementations of the described techniques may include hardware, a method or process, or a computer tangible medium.


In one general aspect, non-transitory computer-readable medium may include one or more instructions that, when executed by one or more processors of a device, cause the device to: configure a resource, deployed in a cloud computing environment, to deploy thereon a sensor, the sensor configured to detect runtime data. Medium may furthermore detect a potential cybersecurity threat on the resource based on detected runtime data received from the sensor. Medium may in addition initiate inspection of the resource for the potential cybersecurity threat. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.


In one general aspect, system may include a processing circuitry. System may also include a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to: configure a resource, deployed in a cloud computing environment, to deploy thereon a sensor, the sensor configured to detect runtime data. System may in addition detect a potential cybersecurity threat on the resource based on detected runtime data received from the sensor. System may moreover initiate inspection of the resource for the potential cybersecurity threat. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.


Implementations may include one or more of the following features. System where the memory contains further instructions which when executed by the processing circuitry further configure the system to: detect a disk associated with the resource; generate an inspectable disk based on the detected disk; and inspect the inspectable disk for a cybersecurity object indicating the potential cybersecurity threat. System where the memory contains further instructions which when executed by the processing circuitry further configure the system to: clone the detected disk into the inspectable disk. System where the memory contains further instructions which when executed by the processing circuitry further configure the system to: determine that the potential cybersecurity threat is an actual cybersecurity threat; and initiate a mitigation action based on the actual cybersecurity threat. System where the memory contains further instructions which when executed by the processing circuitry further configure the system to: apply a logical expression of a definition to an event detected by the sensor; and determine that the potential cybersecurity threat is an actual cybersecurity threat in response to a binary outcome of the applied logical expression having a predetermined value. System where the resource is a virtual machine, and the sensor is a service deployed on an operating system of the virtual machine. System where the resource is a serverless function, and the sensor is a code layer of the serverless function, the serverless function further including a function code. System where the resource is a software container, may include: configuring a container cluster of the software container to deploy a daemonset, the daemonset including a plurality of nodes, each node including a daemonset pod, where the daemonset pod is the deployed sensor. System where the memory contains further instructions which when executed by the processing circuitry further configure the system to: send a rule to the sensor, the rule including a logical expression and an action; configure the sensor to apply the rule on a detected event; and configure the sensor to perform the action in response to applying the rule on the detected event and receiving a predetermined result. System where the memory contains further instructions which when executed by the processing circuitry further configure the system to: send data pertaining to the detected event to a sensor backend server. Implementations of the described techniques may include hardware, a method or process, or a computer tangible medium.





BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter disclosed herein is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other objects, features, and advantages of the disclosed embodiments will be apparent from the following detailed description taken in conjunction with the accompanying drawings.



FIG. 1 is a schematic diagram of a cloud computing environment monitored for a cybersecurity threat by an inspection environment, implemented in accordance with an embodiment.



FIG. 2 is a schematic illustration of a sensor backend server communicating with a plurality of sensors deployed on various workloads, implemented in accordance with an embodiment.



FIG. 3 is a flowchart of a method for performing cybersecurity threat detection on a resource in a cloud computing environment, implemented in accordance with an embodiment.



FIG. 4 is a schematic diagram of a sensor backend server according to an embodiment.



FIG. 5 is a flowchart of a method for mitigating a cybersecurity threat, implemented in accordance with an embodiment.



FIG. 6 is a flowchart of a method for utilizing a security graph in detecting a cybersecurity threat based on an indicator of compromise, implemented in accordance with an embodiment.





DETAILED DESCRIPTION

It is important to note that the embodiments disclosed herein are only examples of the many advantageous uses of the innovative teachings herein. In general, statements made in the specification of the present application do not necessarily limit any of the various claimed embodiments. Moreover, some statements may apply to some inventive features but not to others. In general, unless otherwise indicated, singular elements may be in plural and vice versa with no loss of generality. In the drawings, like numerals refer to like parts through several views.


The various disclosed embodiments include a method and system for providing a sensor deployed on a workload in a cloud computing environment, to complement detection of cybersecurity threats using static analysis techniques. A sensor is a software package executable on a machine, such as an endpoint machine. An endpoint machine (or simply “endpoint”) may be, for example, a proxy, a gateway, a reverse proxy, a webserver, and the like. A sensor is able to deploy on an endpoint utilizing less resources than an agent, as the sensor is configured to retrieve and analyze less data than an agent software is. This is due to the sensor capabilities being complemented by a static analysis solution, such as a cybersecurity threat inspector.


In an embodiment, the sensor is configured to listen to a data link layer. For example, in an embodiment, a sensor is configured to listen for packets utilizing the extended Berkeley Packet Filter (eBPF) interface. In certain embodiments, the sensor is configured to request rules, definitions, and the like, from a sensor backend server. The sensor is configured, for example, to apply a rule from the requested rules, definitions, and the like to an event detected by listening on the eBPF interface of a machine on which the sensor is deployed. In certain embodiments, the sensor is configured to send an event to the sensor backend server, for example in response to determining that the event matches a predefined definition.


In certain embodiments the sensor is configured to send an event, for example based on a predetermined definition, to a sensor backend server, which is configured to store the event on a security graph. The security graph includes a representation of the cloud computing environment in which the endpoint is deployed. For example, the sensor may detect that the endpoint sent a network packet to an IP address which is associated with a known cybersecurity risk, such as a coin mining pool. The sensor is configured to generate a notification to a sensor backend server. In an embodiment, the sensor backend server is configured to generate an instruction for an inspection controller. The inspection controller, in turn, is configured to provision an inspector to inspect the endpoint for the presence of a cryptominer malware.


By performing runtime and static analysis in this manner, the overlap in detection between the sensor and inspector are reduced. Additionally, the sensor is able to initiate inspection by the inspector, which allows efficient prioritizing of inspection resources, thereby reducing time to detection of cybersecurity threats, which also reduces time to respond to cybersecurity threats.



FIG. 1 is an example schematic diagram of a cloud computing environment monitored for a cybersecurity threat by an inspection environment, implemented in accordance with an embodiment. In an embodiment, a cloud computing environment 110 is implemented as a virtual private cloud (VPC), Virtual Network (VNet), and the like, over a cloud computing platform. A cloud computing platform may be provided, for example, by Amazon® Web Services (AWS), Google® Cloud Platform (GCP), Microsoft® Azure, and the like. A cloud computing environment 110 includes cloud entities deployed therein. A cloud entity may be, for example, a principal, a resource, a combination thereof, and the like. In an embodiment, a resource is a cloud entity which provides access to a compute resource, such as a processor, a memory, a storage, and the like. In some embodiments a resource is a virtual machine, a software container, a serverless function, and the like. A resource may be, or may include, a software application deployed thereon, such as a webserver, a gateway, a load balancer, a web application firewall (WAF), an appliance, and the like.


In certain embodiments, a principal is a cloud entity which is authorized to initiate actions in the cloud computing environment. A cloud entity may be, for example, a user account, a service account, a role, and the like. In some embodiments, a cloud entity is a principal relative to another cloud entity, and a resource to other cloud entities. For example, a load balancer is a resource to a user account requesting a webpage from a webserver behind the load balancer, and the load balancer is a principal to the webserver.


The cloud computing environment 110 includes a plurality of resources, such as virtual machine 112, software container orchestrator 114, and serverless function 116. A virtual machine 112 may be deployed, for example, utilizing Oracle® VirtualBox®. A software container orchestrator 114 may be deployed, for example, utilizing a Docker® engine, a Kubernetes® engine, and the like. In an embodiment, a software container orchestrator 114 is configured to deploy a software cluster, each cluster including a plurality of nodes. In an embodiment, a node includes a plurality of pods. A serverless function 116, may be, for example, utilized with Amazon® Lambda. In an embodiment, the serverless function 116 is a serverless function container image.


Each such resource is susceptible to various cybersecurity threats. Such threats can become apparent for example due to a software version of an application in a software container 114, an operating system (OS) version of a virtual machine 112, a misconfiguration in code of a serverless function 116, and the like. The cloud computing environment 110 is monitored for cybersecurity threats by an inspection environment 120. In an embodiment, the inspection environment is implemented as a cloud computing environment, such as a VPC, VNet, and the like.


In an embodiment, each of the virtual machine 112, the software container 114, and the serverless function 116 include a sensor configured to a particular resource, resource type, combination thereof, and the like. An example deployment of a sensor is discussed in more detail in FIG. 2 below.


In an embodiment, the sensor (not shown in FIG. 1) is configured to listen for events, packets, and the like, on a data link layer. For example, the sensor is configured to utilize an eBPF interface, which allows non-intrusive monitoring of the data link layer communication. In certain embodiments, the sensor is further configured to send data to and receive data from a sensor backend server 128. The sensor backend server 128 is a workload, such as a virtual machine, software container, serverless function, combination thereof, and the like, which is deployed in the inspection environment 120.


In an embodiment, the sensor backend server 128 is configured to receive sensor generated data. For example, the sensor backend server 128 is configured, in an embodiment, to receive events from a sensor. In some embodiments, the sensor is configured to request from the sensor backend server 128 rules, definitions, and the like, which the sensor is configured to apply to events, for example as detected on an eBPF interface. For example, a predetermined event, such as indicating access to an IP address, IP address range, and the like, may be checked against a definition. A definition is a logical expression which, when applied to an event, yields a “true” or “false” result. In an embodiment, a rule is a logical expression which includes an action. For example, a rule may be that if a certain definition is true when applied to an event, data pertaining to the event should be sent to the sensor backend server 128.


In some embodiments, the sensor backend server 128 is configured to initiate inspection of a resource deployed in the cloud computing environment 110. For example, the sensor backend server 128 may be configured to initiate such inspection in response to receiving an event, data, a combination thereof, and the like, from a sensor deployed on a resource. In an embodiment, initiating inspection of a resource is performed by generating an instruction for an inspection controller 122, the instruction, when executed, configures an inspector 124 to inspect the resource.


For example, a sensor is configured to send event data to the sensor backend server 128 in response to detecting that a definition, applied by the sensor to a detected event, results in a “true” value when applied. As an example, the definition may be “is the IP address in the range of 127.0.0.1 through 127.0.0.99”, which in this example correspond to an IP address range used by a malware, such as a cryptominer. When the definition is applied, for example to a detected network packet, and the result is “true”, the sensor is configured to send data pertaining to the event to the sensor backend server 128. Data pertaining to the event may be, for example, an IP address, an event type, combinations thereof, and the like.


In an embodiment, the sensor backend server 128 is configured to receive the data. In some embodiments, the sensor backend server 128 is further configured to apply a rule to the received data to determine if an inspection of the workload on which the sensor is deployed should be inspected for a cybersecurity threat. For example, the sensor backend server 128 is configured to generate an instruction to inspect a virtual machine 112, in response to receiving an indication from a sensor deployed as service on the virtual machine that a communication has been detected between the virtual machine 112 and a server having an IP address which is a forbidden IP address, such as an IP address associated with a malware.


For example, the sensor backend server 128 may generate an instruction for the inspection controller 122, which when executed by the inspection controller generates an inspectable disk, for example utilizing a snapshot, a copy, a clone, and the like of a disk (not shown) associated with the virtual machine 112, and provides access to an inspector 124 to the inspectable disk. In an embodiment the inspector 124 is configured to detect a cybersecurity threat. For example, the inspector 124 is configured to receive, in an embodiment, a hash of an application stored on the inspectable disk, and determine if the hash matches a hash of known malware applications. In certain embodiments, the inspector 124 is provided with a persistent volume claim (PVC) to the inspectable disk.


In some embodiments, the sensor is configured to generate a hash of an application on the resource, such as the virtual machine 112, on which it is deployed, and send the hash to the sensor backend server 128. The received hash may then be compared, for example by providing it to the inspector 124, with known hash values which correspond to malware applications.


While the examples above discuss malware and cryptominers, it is readily apparent that the sensor and inspector 124 may be utilized to detect other types of cybersecurity threats, such as an exposure, a vulnerability, a weak password, an exposed password, a misconfiguration, and the like.


In certain embodiments, the inspection environment 120 further includes a graph database 126, on which a security is stored. In an embodiment, the security graph is configured to store a representation of a cloud computing environment, such as cloud computing environment 110. For example, the representation may be based on a predefined unified data schema, so that each different cloud platform may be represented using a unified data schema, allowing for a unified representation. For example, a principal may be represented by a predefined data structure, each principal represented by a node in the security graph. Likewise, a resource may be represented by another predefined data structure, each resource represented by a node in the security graph.


In certain embodiments, data received from a sensor deployed on a resource in the cloud computing environment may be stored in the graph database as part of the security graph. In the example above, in response to receiving data from the sensor which indicates a potential malware infection of the virtual machine 112, the sensor backend server 128 is configured, in an embodiment, to: generate a node representing the malware in the security graph, generate a node in the security graph representing the virtual machine 112, and connect the node representing the malware with the node representing the virtual machine 112.



FIG. 2 is an example schematic illustration of a sensor backend server communicating with a plurality of sensors deployed on various workloads, implemented in accordance with an embodiment. In some embodiments, a sensor backend server 128 is configured to communicate with a machine (not shown) having a sensor installed thereon and communicatively coupled with the sensor backend server 128. In an embodiment, the machine is bare metal machine, a computer device, a networked computer device, a laptop, a tablet, and the like computing devices.


In an embodiment, a sensor backend server 128 is implemented as a virtual machine, a software container, a serverless function, a combination thereof, and the like. In certain embodiments, a plurality of sensor backend servers 128 may be implemented. In some embodiments where a plurality of sensor backend servers 128 are utilized, a first group of sensor backend servers of the plurality of sensor backend servers is configured to communicate with a sensor deployed on a first type of resource (e.g., virtual machine), a second group of sensor backend servers is configured to communicate with resources of a second type, etc. In an embodiment, a first group of sensor backend servers is configured to communicate with sensors deployed on resources in a first cloud computing environment deployed on a first cloud platform (e.g., AWS) and a second group of sensor backend servers is configured to communicate with sensors deployed on resources in a second cloud computing environment deployed on a second cloud platform (e.g., GCP).


A virtual machine 112 includes a sensor 210. In an embodiment, the sensor 210 is deployed as a service executed on the virtual machine 112. In some embodiments, a virtual machine 112 is configured to request binary code, a software package, and the like, for example from a sensor backend sever 128, which when executed by the virtual machine 112 cause a sensor 210 to run as a service on the virtual machine 112. The sensor 210 is configured to listen to a data link layer communication, for example through an eBPF interface.


A container cluster 114 runs a daemonset, and includes a plurality of nodes, such as node 220. The daemonset ensures that each node 220 runs a daemonset pod 222, which is configured as a sensor. For example, a Kubernetes® cluster may execute a daemonset configured to deploy a daemonset pod on each deployed node, wherein the daemonset pod is configured to listen to a data link layer communication, for example through an eBPF interface, to communication of a plurality of pods, such as pod-1224 through pod-N 226, where ‘NI’ is an integer having a value of ‘1’ or greater. The daemonset pod 222 is configured, in an embodiment, to communicate with the sensor backend server 128.


A serverless function 116 includes, in an embodiment, a function code 232, and a plurality of code layers 1 through M (labeled respectively as 234 through 236), where ‘M’ is an integer having a value of ‘1’ or greater. For example, in AWS Lambda a layer contains, in an embodiment, code, content, a combination thereof, and the like. In some embodiments, a layer, such as layer 234 includes runtime data, configuration data, software libraries, and the like.


In certain embodiments, the serverless function 116 includes a sensor layer 238. The sensor layer 238 is configured, in an embodiment, to listen to a data link layer communication of the serverless function 116, for example through an eBPF interface.


The sensor service 210, daemonset pod 222, and sensor layer 238 are each an implementation of a sensor, according to an embodiment. In an embodiment, a sensor is configured to communicate with a sensor backend server 128 through a transport layer protocol, such as TCP. For example, the sensor backend server 128 is configured, in an embodiment, to listen to a predetermined port using a TCP protocol, and a sensor, such as sensor 210, daemonset pod 222, and sensor layer 238 are each configured to communicate with the backend sensor server 128, for example by initiating communication using TCP over the predetermined port.



FIG. 3 is an example flowchart 300 of a method for performing cybersecurity threat detection on a resource in a cloud computing environment, implemented in accordance with an embodiment.


At S310, a resource is provided with a sensor software. In an embodiment, the resource is any one of a virtual machine, a software container, a serverless function, and the like. In certain embodiments, the sensor software is provided based on the resource type. For example, a virtual machine is provided with a software package, such as an executable code, for example a binary code. A software container engine is provided with a daemonset, so that, in an embodiment where a node is deployed in a cluster of the software container engine, the node includes a daemonset pod which is configured to provide the functionality of a sensor, for example such as detailed above. In an embodiment, a serverless function is provided with a sensor layer by providing a code for example in a .ZIP file.


In an embodiment, providing a sensor includes configuring a resource, such as a virtual machine, software container, serverless function, and the like, to receive software which, when executed, configures the resource to deploy a sensor thereon.


At S320, an event is detected from a data link layer communication. In an embodiment, the data link layer is monitored through an eBPF interface for events. In certain embodiments, a software bill of materials (SBOM) is generated. An SBOM may be implemented as a text file, which is based off of events which were detected, for example through the eBPF interface. In an embodiment, an SBOM includes an identifier of a library which is accessed in runtime, an identifier of a binary which is accessed in runtime, an image of which an instance is deployed in runtime, a port which is accessed by a runtime program, a cryptographic hash function value (such as an SHA1, SHA2, and the like values), and the like. For example, an SBOM may include:
















programs {



 exe_name: “/usr/sbin/rpc.mountd”



 last_seen: 1663138800



 exe_size: 133664



 exe_sha1: “200f06c12975399a4d7a32e171caabfb994f78b9”



 modules {



  path: “/usr/lib/libresolv-2.32.so”



  last_seen: 1663138800



 }



 modules {



  path: “/usr/lib/libpthread-2.32.so”



  last_seen: 1663138800



 }



 modules {



  path: “/usr/lib/ld-2.32. so”



  last_seen: 1663138800



 }



 modules {



  path: “/usr/lib/libc-2.32. so”



  last_seen: 1663138800



 }



 modules {



  path: “/usr/lib/libtirpc.so.3.0.0”



  last_seen: 1663138800



 }



 modules {



  path: “/usr/lib/libnss_files-2.32. so”



  last_seen: 1663138800



 }



 modules {



  path: “/usr/sbin/rpc.mountd”



  last_seen: 1663138800



 }



 listening_sockets {



  ip_addr: “0.0.0.0”



  port: 60311



 }



 listening_sockets {



  ip_addr: “0.0.0.0”



  port: 43639



 }










This portion of an SBOM indicates that a remote procedure call (RPC) is executed, which is configured to receive a client request to mount a file system.


At S330, the event is matched to a definition. In some embodiments, a definition includes a logical expression, which when applied to an event results in a “true” or “false” value. For example, a definition may state “software library xyz is accessed”, with a result being either true or false, when applied to an event. In some embodiments, a rule is applied to an event. In an embodiment, a rule is a logical expression which further includes an action. For example, a rule states, in an embodiment, “IF software library xyz is accessed by UNKNOWN SOFTWARE, generate an alert”. In this example, where an event is detected in which a software having an unknown identifier, for example which does not match a list of preapproved identifiers, attempts to access software library xyz, an alert is generated to indicate that such access is performed.


At S340, a check is performed to determine if data should be transmitted to an inspection environment. In some embodiments, the check is performed by applying a rule to an event, and determining transmission based on an output of applying the rule. If ‘yes’, execution continues at S350, if ‘no’ execution continues at S360.


At S350, data respective of an event is transmitted to an inspection environment. In an embodiment, the data is based on an SBOM file. In some embodiments, the data includes event data, such as an identifier of a resource (e.g., virtual machine, software container, serverless function, etc.), an identifier of an application, a hash value, a uniform resource locator (URL) request, a software library identifier, a software binary file identifier, a timestamp, and the like.


At S360, a check is performed to determine if monitoring of the resource should continue. For example, a daemonset of a container may be configured to periodically deploy a daemonset pod to monitor pods in a node. As another example, a virtual machine may be configured to periodically deploy a sensor service which runs as a process on the virtual machine, terminate the process after a predetermined period of time, terminate the process after a predetermined number of detected events, and the like. In some embodiments, the check is performed based on a predetermined amount of elapsed time (e.g., every four hours, every day, twice a day, etc.). If ‘yes’, execution continues at S320. If ‘no’, in an embodiment execution terminates. In some embodiments, if ‘no’, another check is performed at S360, for example after a predetermined period of time has lapsed.



FIG. 4 is an example schematic diagram of a sensor backend server 128 according to an embodiment. The sensor backend server 128 includes a processing circuitry 410 coupled to a memory 420, a storage 430, and a network interface 440. In an embodiment, the components of the sensor backend server 128 may be communicatively connected via a bus 450.


The processing circuitry 410 may be realized as one or more hardware logic components and circuits. For example, and without limitation, illustrative types of hardware logic components that can be used include field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), Application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), graphics processing units (GPUs), tensor processing units (TPUs), general-purpose microprocessors, microcontrollers, digital signal processors (DSPs), and the like, or any other hardware logic components that can perform calculations or other manipulations of information.


The memory 420 may be volatile (e.g., random access memory, etc.), non-volatile (e.g., read only memory, flash memory, etc.), or a combination thereof.


In one configuration, software for implementing one or more embodiments disclosed herein may be stored in the storage 430. In another configuration, the memory 420 is configured to store such software. Software shall be construed broadly to mean any type of instructions, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. Instructions may include code (e.g., in source code format, binary code format, executable code format, or any other suitable format of code). The instructions, when executed by the processing circuitry 410, cause the processing circuitry 410 to perform the various processes described herein.


The storage 430 may be magnetic storage, optical storage, and the like, and may be realized, for example, as flash memory or other memory technology, compact disk-read only memory (CD-ROM), Digital Versatile Disks (DVDs), or any other medium which can be used to store the desired information.


The network interface 440 allows the sensor backend server 128 to communicate with, for example, a sensor 210, a daemonset pod 222, a sensor layer 238, and the like.


It should be understood that the embodiments described herein are not limited to the specific architecture illustrated in FIG. 4, and other architectures may be equally used without departing from the scope of the disclosed embodiments.


Furthermore, in certain embodiments the inspection controller 122, inspector 124, and the like, may be implemented with the architecture illustrated in FIG. 4. In other embodiments, other architectures may be equally used without departing from the scope of the disclosed embodiments.



FIG. 5 is an example flowchart 500 of a method for mitigating a cybersecurity threat, implemented in accordance with an embodiment.


At S510, an instruction to perform inspection is generated. In an embodiment, inspection is performed on a resource, which may be, for example, a virtual machine, a software container, a serverless function, and the like. In an embodiment, the instruction, when executed, generates an inspectable disk based on a disk of a resource. For example, in an embodiment an inspectable disk is generated by performing a snapshot, a clone, a copy, a duplicate, and the like, of a disk attached to a virtual machine. The inspectable disk is accessible by an inspector. In an embodiment, the inspector utilizes static analysis techniques, for example to detect cybersecurity objects, such as a password, a certificate, an application binary, a software library, a hash, and the like.


The detected cybersecurity objects, cybersecurity threats, and the like, are represented, in an embodiment, in a security graph. For example, a node is generated in an embodiment to represent a malware object. The node representing the malware object is connected to a node representing the resource on which an inspector detected the malware object, to indicate that the malware object is present on the resource.


At S520, a cybersecurity threat is detected. In an embodiment, a cybersecurity threat is detected in response to detecting a cybersecurity object on a disk. In certain embodiments, a cybersecurity threat is an exposure, a vulnerability, a misconfiguration, a malware code object, a hash, a combination thereof, and the like. In some embodiments, a hash, which is detected or generated, is compared to another hash of a list of hashes which indicate know cybersecurity threats. For example, malware code objects are often detected by generating hashes of code objects and comparing them to hashes stored in a database of known hashes which are associated with malicious software. In certain embodiments, the cybersecurity threat is a potential cybersecurity threat. In an embodiment, runtime data is utilized to determine if the potential cybersecurity threat is an actual cybersecurity threat.


At S530, runtime data is received. In an embodiment, the runtime data is received from the inspected resource. In certain embodiments, runtime data is received based on cybersecurity objects detected by static analysis methods performed on the resource. For example, an inspector accessing an inspectable disk which is generated based on a disk of a virtual machine deployed in a cloud computing environment detects application libraries, which are cybersecurity objects. In an embodiment a definition is generated based on the detected cybersecurity objects. For example, a cybersecurity object may be a binary of application “xyz”. A definition is generated based on the detected cybersecurity object, for example “Application xyz is deployed in runtime”. In an embodiment, a rule is generated, for example based on the definition, further stating “IF application xyz is deployed in runtime, THEN perform mitigation action”.


At S540, an instruction to perform a mitigation action is generated. In an embodiment, the instruction, when executed, initiates a mitigation action in the cloud computing environment in which the resource is deployed. In some embodiments, the mitigation action is generated based on the detected cybersecurity threat and the received runtime data. In certain embodiments, the mitigation action includes generating an alert, assigning a severity score to an alert (e.g., low, moderate, severe, critical), modifying a severity score of an alert, and the like.


While static analysis techniques can detect such cybersecurity objects and threats, runtime data is required to determine if the cybersecurity objects and threats are actually present in runtime. For example, a database having a misconfiguration, such as no password protection, is considered a cybersecurity threat. Typically, an alert is generated in response to detecting such a cybersecurity threat, and a mitigation action is initiated. However, in cloud computing production environments many such alerts are generated, and therefore it is desirable to prioritize alerts based, for example, on a severity of an event. In this example, if a process for managing the database is not present at runtime, then the severity of the cybersecurity threat is actually lower than if the database software was running, and therefore presented an actual cybersecurity threat. It is therefore beneficial to combine static analysis data with runtime data in an efficient manner in order to prioritize responses, such as mitigation actions, to detected cybersecurity threats. This allows to better utilize the compute resources of a cloud computing environment, and improving response time to cybersecurity threats based on actual severity.



FIG. 6 is an example flowchart 600 of a method for utilizing a security graph in detecting a cybersecurity threat based on an indicator of compromise, implemented in accordance with an embodiment.


At S610, an indicator of compromise (IOC) is received. In an embodiment, the IOC is received from a sensor, the sensor configured to detect an IOC. In certain embodiments, an IOC is data, such as network traffic data, login data, access data, a data request, and the like. For example, IOC data indicates, in an embodiment, unusual network traffic, unusual login time, unusual logged-in user session time, a high volume of requests for data, network traffic to restricted domains, network traffic to suspicious geographical domains, mismatched port-application network traffic (i.e. sending command and control communication as a DNS request over port 80), and the like.


In certain embodiments, an IOC data is generated based on an aggregation of events detected on a resource, for example on a virtual machine. In an embodiment, a sensor is configured to store a plurality of events, and generate aggregated data based on the stored plurality of events. For example, network traffic destinations are stored, in an embodiment, to perform anomaly detection, i.e., to detect network traffic destinations which are anomalous.


At S620, a security graph is traversed to detect a cybersecurity threat. In an embodiment, an instruction is generated which, when executed by a graph database, configures a database management system to execute a query for detecting a node in a security graph stored on the graph database. In certain embodiments, the detected node represents a resource on which a sensor is deployed, the sensor generating the IOC data which is received at S610.


In certain embodiments, a security graph is traversed to detect a node representing a cybersecurity threat corresponding to the IOC and connected to a node representing the resource from which the IOC was generated. For example, a query is generated based on the IOC data and executed on the security graph. In an embodiment, execution of the query returns a result.


At S630, a check is performed to determine if the cybersecurity threat was found. In an embodiment, the check includes receiving a result from a query executed on a security graph, and determining if a node representing a resource is connected to a node representing a cybersecurity threat. If ‘yes’, execution continues at S660. If ‘no’ execution continues at S640.


At S640, a node is generated to represent the IOC in the security graph. In an embodiment, IOC data is stored with the node. In certain embodiments, an identifier of an IOC may be assigned to the IOC data, and the identifier of the IOC is stored with the node in the graph database.


At S650, an edge is generated to connect the node representing the IOC to a node representing the resource. In an embodiment the resource is a resource from which the IOC originated. For example, an edge may be generated to connected the node representing the IOC to the node representing the resource.


At S660, a mitigation action is generated. In an embodiment, generating a mitigation action includes generating an instruction which when executed configures a computing device to initiate the mitigation action. In an embodiment, the mitigation is initiating an inspection of the resource, generating alert an alert, a combination thereof, and the like. In certain embodiments the alert is generated based on any one of: the IOC data, an identifier of the resource, a predetermined rule, a combination thereof, and the like. In an embodiment, initiating inspection of a resource includes generating an instruction which when executed in a cloud computing environment configures the cloud computing environment to generate an inspectable disk, and provide an inspector workload access to the inspectable disk to inspect the inspectable disk for a cybersecurity threat corresponding to the IOC data.


The various embodiments disclosed herein can be implemented as hardware, firmware, software, or any combination thereof. Moreover, the software is preferably implemented as an application program tangibly embodied on a program storage unit or computer readable medium consisting of parts, or of certain devices and/or a combination of devices. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture. Preferably, the machine is implemented on a computer platform having hardware such as one or more central processing units (“CPUs”), a memory, and input/output interfaces. The computer platform may also include an operating system and microinstruction code. The various processes and functions described herein may be either part of the microinstruction code or part of the application program, or any combination thereof, which may be executed by a CPU, whether or not such a computer or processor is explicitly shown. In addition, various other peripheral units may be connected to the computer platform such as an additional data storage unit and a printing unit. Furthermore, a non-transitory computer readable medium is any computer readable medium except for a transitory propagating signal.


All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the principles of the disclosed embodiment and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the disclosed embodiments, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure.


It should be understood that any reference to an element herein using a designation such as “first,” “second,” and so forth does not generally limit the quantity or order of those elements. Rather, these designations are generally used herein as a convenient method of distinguishing between two or more elements or instances of an element. Thus, a reference to first and second elements does not mean that only two elements may be employed there or that the first element must precede the second element in some manner. Also, unless stated otherwise, a set of elements comprises one or more elements.


As used herein, the phrase “at least one of” followed by a listing of items means that any of the listed items can be utilized individually, or any combination of two or more of the listed items can be utilized. For example, if a system is described as including “at least one of A, B, and C,” the system can include A alone; B alone; C alone; 2A; 2B; 2C; 3A; A and B in combination; B and C in combination; A and C in combination; A, B, and C in combination; 2A and C in combination; A, 3B, and 2C in combination; and the like.

Claims
  • 1. A method for improved endpoint detection and response (EDR) in a cloud computing environment, comprising: deploying a sensor on a resource, the resource deployed in a cloud computing environment, wherein the sensor is configured to detect runtime data of the resource;detecting a potential cybersecurity threat on the resource based on detected runtime data received from the sensor; andinitiating inspection of the resource for the potential cybersecurity threat.
  • 2. The method of claim 1, further comprising: detecting a disk associated with the resource;generating an inspectable disk based on the detected disk; andinspecting the inspectable disk for a cybersecurity object indicating the potential cybersecurity threat.
  • 3. The method of claim 2, further comprising: cloning the detected disk into the inspectable disk.
  • 4. The method of claim 1, further comprising: determining that the potential cybersecurity threat is an actual cybersecurity threat; andinitiating a mitigation action in the cloud computing environment based on the actual cybersecurity threat.
  • 5. The method of claim 4, further comprising: applying a logical expression of a definition to an event detected by the sensor; anddetermining that the potential cybersecurity threat is an actual cybersecurity threat in response to a binary outcome of the applied logical expression having a predetermined value.
  • 6. The method of claim 1, wherein the resource is a virtual machine, and the sensor is a service deployed on an operating system of the virtual machine.
  • 7. The method of claim 1, wherein the resource is a serverless function, and the sensor is a code layer of the serverless function, the serverless function further including a function code.
  • 8. The method of claim 1, wherein the resource is a software container, further comprising: configuring a container cluster of the software container to deploy a daemonset, the daemonset including a plurality of nodes, each node including a daemonset pod, wherein the daemonset pod is the deployed sensor.
  • 9. The method of claim 1, further comprising: sending a rule to the sensor, the rule including a logical expression and an action;configuring the sensor to apply the rule on a detected event; andconfiguring the sensor to perform the action in response to applying the rule on the detected event and receiving a predetermined result.
  • 10. The method of claim 9, further comprising: sending data pertaining to the detected event to a sensor backend server.
  • 11. A non-transitory computer-readable medium storing a set of instructions for improved endpoint detection and response (EDR) in a cloud computing environment, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to:deploy a sensor on a resource, the resource deployed in a cloud computing environment, wherein the sensor is configured to detect runtime data of the resource;detect a potential cybersecurity threat on the resource based on detected runtime data received from the sensor; andinitiate inspection of the resource for the potential cybersecurity threat.
  • 12. A system for improved endpoint detection and response (EDR) in a cloud computing environment comprising: a processing circuitry; anda memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:deploy a sensor on a resource, the resource deployed in a cloud computing environment, wherein the sensor is configured to detect runtime data of the resource;detect a potential cybersecurity threat on the resource based on detected runtime data received from the sensor; andinitiate inspection of the resource for the potential cybersecurity threat.
  • 13. The system of claim 12, wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to: detect a disk associated with the resource;generate an inspectable disk based on the detected disk; andinspect the inspectable disk for a cybersecurity object indicating the potential cybersecurity threat.
  • 14. The system of claim 13, wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to: clone the detected disk into the inspectable disk.
  • 15. The system of claim 12, wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to: determine that the potential cybersecurity threat is an actual cybersecurity threat; andinitiate a mitigation action in the cloud computing environment based on the actual cybersecurity threat.
  • 16. The system of claim 15, wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to: apply a logical expression of a definition to an event detected by the sensor; anddetermine that the potential cybersecurity threat is an actual cybersecurity threat in response to a binary outcome of the applied logical expression having a predetermined value.
  • 17. The system of claim 12, wherein the resource is a virtual machine, and the sensor is a service deployed on an operating system of the virtual machine.
  • 18. The system of claim 12, wherein the resource is a serverless function, and the sensor is a code layer of the serverless function, the serverless function further including a function code.
  • 19. The system of claim 12, wherein the resource is a software container, further comprising: configuring a container cluster of the software container to deploy a daemonset, the daemonset including a plurality of nodes, each node including a daemonset pod, wherein the daemonset pod is the deployed sensor.
  • 20. The system of claim 12, wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to: send a rule to the sensor, the rule including a logical expression and an action;configure the sensor to apply the rule on a detected event; andconfigure the sensor to perform the action in response to applying the rule on the detected event and receiving a predetermined result.
  • 21. The system of claim 20, wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to: send data pertaining to the detected event to a sensor backend server.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Non-Provisional patent application Ser. No. 18/045,046 filed Oct. 7, 2022, the contents of which are hereby incorporated by reference.

US Referenced Citations (337)
Number Name Date Kind
6910132 Bhattacharya Jun 2005 B1
7627652 Commons et al. Dec 2009 B1
7784101 Verbowski et al. Aug 2010 B2
8200965 Fujibayashi et al. Jun 2012 B2
8352431 Protopopov et al. Jan 2013 B1
8412688 Armangau et al. Apr 2013 B1
8413239 Sutton Apr 2013 B2
8417967 Foster et al. Apr 2013 B2
8499354 Satish et al. Jul 2013 B1
8595822 Schrecker et al. Nov 2013 B2
8701200 Naldurg et al. Apr 2014 B2
8789049 Hutchins et al. Jul 2014 B2
8813234 Bowers et al. Aug 2014 B1
8898481 Osburn, III et al. Nov 2014 B1
8904525 Hodgman et al. Dec 2014 B1
8914406 Haugsnes Dec 2014 B1
9009836 Yarykin Apr 2015 B1
9094379 Miller Jul 2015 B1
9119017 Sinha Aug 2015 B2
9165142 Sanders et al. Oct 2015 B1
9172621 Dippenaar Oct 2015 B1
9185136 Dulkin et al. Nov 2015 B2
9330273 Khetawat et al. May 2016 B2
9369433 Paul Jun 2016 B1
9419996 Porat Aug 2016 B2
9438634 Ross et al. Sep 2016 B1
9467473 Jayaraman Oct 2016 B2
9544327 Sharma et al. Jan 2017 B1
9563385 Kowalski et al. Feb 2017 B1
9569328 Pavlov et al. Feb 2017 B2
9582662 Messick et al. Feb 2017 B1
9596235 Badam et al. Mar 2017 B2
9607104 Turner et al. Mar 2017 B1
9646172 Hahn May 2017 B1
9661009 Karandikar et al. May 2017 B1
9672355 Titonis et al. Jun 2017 B2
9712503 Ahmed Jul 2017 B1
9892261 Joram Feb 2018 B2
10002247 Suarez et al. Jun 2018 B2
10032032 Suarez et al. Jul 2018 B2
10135826 Reddy Nov 2018 B2
10229125 Goodman et al. Mar 2019 B2
10255370 Carpenter et al. Apr 2019 B2
10360025 Foskett et al. Jul 2019 B2
10412103 Haugsnes Sep 2019 B2
10412109 Loureiro Sep 2019 B2
10459664 Dreier et al. Oct 2019 B1
10536471 Derbeko et al. Jan 2020 B1
10540499 Wailly Jan 2020 B2
10552610 Vashisht et al. Feb 2020 B1
10554507 Siddiqui et al. Feb 2020 B1
10567468 Perlmutter Feb 2020 B2
10572226 Biskup et al. Feb 2020 B2
10574675 Peppe et al. Feb 2020 B2
10630642 Clark et al. Apr 2020 B2
10664619 Marelas May 2020 B1
10691636 Tabaaloute et al. Jun 2020 B2
10721260 Schlarp et al. Jul 2020 B1
10725775 Suarez et al. Jul 2020 B2
10735430 Stoler Aug 2020 B1
10735442 Swackhamer Aug 2020 B1
10791138 Siddiqui Sep 2020 B1
10803188 Rajput et al. Oct 2020 B1
10831898 Wagner Nov 2020 B1
10915626 Tang Feb 2021 B2
10924503 Pereira et al. Feb 2021 B1
10972484 Swackhamer Apr 2021 B1
10997293 Wiest et al. May 2021 B2
11005860 Glyer et al. May 2021 B1
11016954 Babocichin et al. May 2021 B1
11044118 Reed et al. Jun 2021 B1
11055414 Claes Jul 2021 B2
11064032 Yang et al. Jul 2021 B1
11099976 Khakare et al. Aug 2021 B2
11102231 Kraning et al. Aug 2021 B2
11165652 Byrne Nov 2021 B1
11245730 Bailey Feb 2022 B2
11271961 Berger Mar 2022 B1
11334670 Franco May 2022 B2
11388183 Hoopes et al. Jul 2022 B2
11397808 Prabhu et al. Jul 2022 B1
11405426 Nguyen Aug 2022 B2
11444974 Shakhzadyan Sep 2022 B1
11483317 Bolignano et al. Oct 2022 B1
11496498 Wright et al. Nov 2022 B2
11503063 Rao Nov 2022 B2
11507672 Pagnozzi et al. Nov 2022 B1
11516222 Srinivasan et al. Nov 2022 B1
11546360 Woodford Jan 2023 B2
11556659 Kumar et al. Jan 2023 B1
11558401 Vashisht et al. Jan 2023 B1
11558423 Gordon et al. Jan 2023 B2
11567751 Cosentino et al. Jan 2023 B2
11570090 Shen et al. Jan 2023 B2
11575696 Ithal et al. Feb 2023 B1
11606378 Delpont et al. Mar 2023 B1
11645390 Vijayvargiya May 2023 B2
11662928 Kumar et al. May 2023 B1
11663340 Wu et al. May 2023 B2
11669386 Abrol Jun 2023 B1
11700233 St. Pierre Jul 2023 B2
11757844 Xiao Sep 2023 B2
11770398 Erlingsson Sep 2023 B1
11792284 Nanduri Oct 2023 B1
11799874 Lichtenstein et al. Oct 2023 B1
11803766 Srinivasan Oct 2023 B1
11831670 Molls et al. Nov 2023 B1
11841945 Fogel Dec 2023 B1
11914707 Ramanathan et al. Feb 2024 B1
11922220 Haghighat et al. Mar 2024 B2
11936785 Shemesh et al. Mar 2024 B1
12019770 Nilsson et al. Jun 2024 B2
12050696 Pieno et al. Jul 2024 B2
12058177 Crabtree et al. Aug 2024 B2
20030188194 Currie et al. Oct 2003 A1
20030217039 Kurtz et al. Nov 2003 A1
20050050365 Seki et al. Mar 2005 A1
20050251863 Sima Nov 2005 A1
20050283645 Turner et al. Dec 2005 A1
20070271360 Sahita et al. Nov 2007 A1
20080075283 Takahashi Mar 2008 A1
20080221833 Brown et al. Sep 2008 A1
20080307020 Ko et al. Dec 2008 A1
20090106256 Safari et al. Apr 2009 A1
20090271863 Govindavajhala et al. Oct 2009 A1
20100242082 Keene et al. Sep 2010 A1
20100281275 Lee et al. Nov 2010 A1
20110055361 Dehaan Mar 2011 A1
20110276806 Casper et al. Nov 2011 A1
20120110651 Van Biljon et al. May 2012 A1
20120297206 Nord et al. Nov 2012 A1
20130024940 Hutchins et al. Jan 2013 A1
20130054890 Desai et al. Feb 2013 A1
20130124669 Anderson et al. May 2013 A1
20130160119 Sartin Jun 2013 A1
20130160129 Sartin Jun 2013 A1
20130290708 Diaz et al. Oct 2013 A1
20140096134 Barak Apr 2014 A1
20140115578 Cooper et al. Apr 2014 A1
20140237537 Manmohan Aug 2014 A1
20140317677 Vaidya Oct 2014 A1
20140337613 Martini Nov 2014 A1
20150033305 Shear Jan 2015 A1
20150055647 Roberts Feb 2015 A1
20150095995 Bhalerao Apr 2015 A1
20150163192 Jain Jun 2015 A1
20150172321 Kirti et al. Jun 2015 A1
20150254364 Piduri et al. Sep 2015 A1
20150310215 McBride et al. Oct 2015 A1
20150319160 Ferguson et al. Nov 2015 A1
20160078231 Bach et al. Mar 2016 A1
20160103669 Gamage et al. Apr 2016 A1
20160105454 Li Apr 2016 A1
20160140352 Nickolov May 2016 A1
20160156664 Nagaratnam Jun 2016 A1
20160224600 Munk Aug 2016 A1
20160299708 Yang et al. Oct 2016 A1
20160366185 Lee et al. Dec 2016 A1
20170026416 Carpenter et al. Jan 2017 A1
20170070506 Reddy Mar 2017 A1
20170104755 Arregoces Apr 2017 A1
20170111384 Loureiro et al. Apr 2017 A1
20170180421 Shieh et al. Jun 2017 A1
20170185784 Madou Jun 2017 A1
20170187743 Madou Jun 2017 A1
20170223024 Desai Aug 2017 A1
20170230179 Mannan et al. Aug 2017 A1
20170237560 Mueller et al. Aug 2017 A1
20170257347 Yan Sep 2017 A1
20170285978 Manasse Oct 2017 A1
20170300690 Ladnai et al. Oct 2017 A1
20170034198 Powers et al. Dec 2017 A1
20170374136 Ringdahl Dec 2017 A1
20180004950 Gupta et al. Jan 2018 A1
20180026995 Dufour Jan 2018 A1
20180027009 Santos Jan 2018 A1
20180063290 Yang et al. Mar 2018 A1
20180081640 Collins Mar 2018 A1
20180150412 Manasse May 2018 A1
20180159882 Brill Jun 2018 A1
20180181310 Feinberg et al. Jun 2018 A1
20180191726 Luukkala Jul 2018 A1
20180219888 Apostolopoulos Aug 2018 A1
20180234459 Kung Aug 2018 A1
20180239902 Godard Aug 2018 A1
20180260566 Chaganti et al. Sep 2018 A1
20180270268 Gorodissky et al. Sep 2018 A1
20180278639 Bernstein et al. Sep 2018 A1
20180288129 Joshi et al. Oct 2018 A1
20180309747 Sweet et al. Oct 2018 A1
20180321993 McClory Nov 2018 A1
20180341768 Marshall et al. Nov 2018 A1
20180359058 Kurian Dec 2018 A1
20180359059 Kurian Dec 2018 A1
20180367548 Stokes, III et al. Dec 2018 A1
20190007271 Rickards et al. Jan 2019 A1
20190018961 Kostyushko et al. Jan 2019 A1
20190043201 Strong Feb 2019 A1
20190058722 Levin et al. Feb 2019 A1
20190068617 Coleman Feb 2019 A1
20190068627 Thampy Feb 2019 A1
20190081963 Waghorn Mar 2019 A1
20190104140 Gordeychik et al. Apr 2019 A1
20190116111 Izard et al. Apr 2019 A1
20190121986 Stopel et al. Apr 2019 A1
20190132350 Smith et al. May 2019 A1
20190149604 Jahr May 2019 A1
20190166129 Gaetjen et al. May 2019 A1
20190171811 Daniel Jun 2019 A1
20190191417 Baldemair et al. Jun 2019 A1
20190205267 Richey et al. Jul 2019 A1
20190207966 Vashisht et al. Jul 2019 A1
20190220575 Boudreau et al. Jul 2019 A1
20190245883 Gorodissky et al. Aug 2019 A1
20190260764 Humphrey et al. Aug 2019 A1
20190278928 Rungta et al. Sep 2019 A1
20190354675 Gan et al. Nov 2019 A1
20190377988 Qi et al. Dec 2019 A1
20200007314 Vouk et al. Jan 2020 A1
20200007569 Dodge et al. Jan 2020 A1
20200012659 Dageville et al. Jan 2020 A1
20200012818 Levin et al. Jan 2020 A1
20200028862 Lin Jan 2020 A1
20200044916 Kaufman et al. Feb 2020 A1
20200050440 Chuppala et al. Feb 2020 A1
20200082094 McAllister et al. Mar 2020 A1
20200106782 Sion Apr 2020 A1
20200125352 Kannan Apr 2020 A1
20200145405 Bosch et al. May 2020 A1
20200244678 Shua Jul 2020 A1
20200244692 Shua Jul 2020 A1
20200259852 Wolff et al. Aug 2020 A1
20200320845 Livny et al. Oct 2020 A1
20200336489 Wuest et al. Oct 2020 A1
20200382556 Woolward et al. Dec 2020 A1
20200387357 Mathon et al. Dec 2020 A1
20200389431 St. Pierre Dec 2020 A1
20200389469 Litichever et al. Dec 2020 A1
20200409741 Dornemann et al. Dec 2020 A1
20210014265 Hadar et al. Jan 2021 A1
20210026932 Boudreau et al. Jan 2021 A1
20210042263 Zdornov et al. Feb 2021 A1
20210105304 Kraning et al. Apr 2021 A1
20210144517 Guim Bernat et al. May 2021 A1
20210149788 Downie May 2021 A1
20210158835 Hill et al. May 2021 A1
20210168150 Ross et al. Jun 2021 A1
20210176123 Plamondon Jun 2021 A1
20210176164 Kung et al. Jun 2021 A1
20210185073 Ewaida et al. Jun 2021 A1
20210200881 Joshi et al. Jul 2021 A1
20210203684 Maor et al. Jul 2021 A1
20210211453 Cooney Jul 2021 A1
20210218567 Richards et al. Jul 2021 A1
20210226812 Park Jul 2021 A1
20210226928 Crabtree et al. Jul 2021 A1
20210234889 Burle et al. Jul 2021 A1
20210263802 Gottemukkula et al. Aug 2021 A1
20210297447 Crabtree et al. Sep 2021 A1
20210306416 Mukhopadhyay et al. Sep 2021 A1
20210314342 Oberg Oct 2021 A1
20210334386 AlGhamdi et al. Oct 2021 A1
20210357246 Kumar et al. Nov 2021 A1
20210360032 Crabtree et al. Nov 2021 A1
20210368045 Verma Nov 2021 A1
20210382995 Massiglia et al. Dec 2021 A1
20210382997 Yi et al. Dec 2021 A1
20210406365 Neil et al. Dec 2021 A1
20210409486 Martinez Dec 2021 A1
20220012771 Gustafson Jan 2022 A1
20220030020 Huffman Jan 2022 A1
20220053011 Rao et al. Feb 2022 A1
20220060497 Crabtree et al. Feb 2022 A1
20220086173 Yavo et al. Mar 2022 A1
20220131888 Kanso Apr 2022 A1
20220179964 Qiao et al. Jun 2022 A1
20220182403 Mistry Jun 2022 A1
20220188273 Koorapati et al. Jun 2022 A1
20220197926 Passey et al. Jun 2022 A1
20220210053 Du Jun 2022 A1
20220215101 Rioux et al. Jul 2022 A1
20220232024 Kapoor Jul 2022 A1
20220232042 Crabtree et al. Jul 2022 A1
20220247791 Duminuco et al. Aug 2022 A1
20220284362 Bellinger et al. Sep 2022 A1
20220309166 Shenoy et al. Sep 2022 A1
20220327119 Gasper et al. Oct 2022 A1
20220342690 Shua Oct 2022 A1
20220342997 Watanabe et al. Oct 2022 A1
20220345481 Shua Oct 2022 A1
20220350931 Shua Nov 2022 A1
20220357992 Karpovsky Nov 2022 A1
20220374519 Botelho et al. Nov 2022 A1
20220400128 Kfir et al. Dec 2022 A1
20220407841 Karpowicz Dec 2022 A1
20220407889 Narigapalli et al. Dec 2022 A1
20220413879 Passey et al. Dec 2022 A1
20220414103 Upadhyay et al. Dec 2022 A1
20220417011 Shua Dec 2022 A1
20220417219 Sheriff Dec 2022 A1
20230007014 Narayan Jan 2023 A1
20230011957 Panse et al. Jan 2023 A1
20230040635 Narayan Feb 2023 A1
20230075355 Twigg Mar 2023 A1
20230087093 Ithal et al. Mar 2023 A1
20230093527 Shua Mar 2023 A1
20230095756 Wilkinson et al. Mar 2023 A1
20230110080 Hen Apr 2023 A1
20230123477 Luttwak et al. Apr 2023 A1
20230125134 Raleigh et al. Apr 2023 A1
20230134674 Quinn et al. May 2023 A1
20230135240 Cody et al. May 2023 A1
20230136839 Sundararajan et al. May 2023 A1
20230161614 Herzberg et al. May 2023 A1
20230164148 Narayan May 2023 A1
20230164164 Herzberg et al. May 2023 A1
20230164182 Kothari et al. May 2023 A1
20230169165 Williams et al. Jun 2023 A1
20230171271 Williams et al. Jun 2023 A1
20230192418 Horowitz et al. Jun 2023 A1
20230208870 Yellapragada et al. Jun 2023 A1
20230224319 Isoyama et al. Jul 2023 A1
20230231867 Rampura Venkatachar Jul 2023 A1
20230237068 Sillifant et al. Jul 2023 A1
20230254330 Singh Aug 2023 A1
20230297666 Atamli et al. Sep 2023 A1
20230336550 Lidgi et al. Oct 2023 A1
20230336578 Lidgi et al. Oct 2023 A1
20230376586 Shemesh et al. Nov 2023 A1
20240007492 Shen et al. Jan 2024 A1
20240037229 Pabón et al. Feb 2024 A1
20240045838 Reiss et al. Feb 2024 A1
20240073115 Chakraborty et al. Feb 2024 A1
20240080329 Reed et al. Mar 2024 A1
20240080332 Ganesh Mar 2024 A1
20240146818 Cody et al. May 2024 A1
20240241752 Crabtree et al. Jul 2024 A1
Foreign Referenced Citations (4)
Number Date Country
4160983 Apr 2023 EP
4254869 Oct 2023 EP
2421792 Jun 2011 RU
10202009702X Apr 2021 SG
Non-Patent Literature Citations (26)
Entry
Ali Gholami; Security and Privacy of Sensitive Data in Cloud Computing: A Survey of Recent Developments; ARIX:2016; pp. 131-150.
Christos Kyrkou; Towards artificial-intelligence-based cybersecurity for robustifying automated driving systems against camera sensor attacks; IEEE 2020; pp. 476-481.
Guo, yu et al. Enabling Encrypted Rich Queries in Distributed Key-Value Stores. IEEE Transactions on Parallel and Distributed Systems, vol. 30, Issue: 6. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8567979 (Year: 2019).
Henry Hanping Feng; Anomaly Detection Using Call Stack Information; IEEE: Year:2003; pp. 1-14.
International Search Report for PCT Application No. PCT/IB2022/060940 dated Feb. 1, 2023. The International Bureau of WIPO.
International Search Report for PCT/IB2023/050848, dated May 9, 2023. International Bureau of WIPO.
International Search Report of PCT/IB2023/058074, dated Nov. 20, 2023. Searching Authority United States Patent and Trademark Office, Alexandria, Virginia.
International Search Report, PCT/IB23/55312. ISA/US, Commissioner for Patents, Alexandria, Virginia. Dated Aug. 30, 2023.
Kumar, Anuj et al. A New Approach for Security in Cloud Data Storage for IOT Applications Using Hybrid Cryptography Technique. 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9087010 (Year: 2020).
Microsoft Build. “Introduction to Azure managed disks”. Aug. 21, 2023, https://docs.microsoft.com/en-us/azure/virtual-machines/managed-disks-overview.
Microsoft Docs. “Create a VM from a managed image”. Article. Jan. 5, 2022. https://docs.microsoft.com/en-us/azure/virtual-machines/windows/create-vm-generalized-managed.
Mishra, Bharati; Jena, Debasish et al. Securing Files in the Cloud. 2016 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7819669 (Year: 2016).
Shuvo, Arfatul Mowla et al. Storage Efficient Data Security Model for Distributed Cloud Storage. 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9356962 (Year: 2020).
Written Opinion of the International Searching Authority for PCT Application No. PCT/IB2022/060940 dated Feb. 1, 2023. The International Bureau of WIPO.
Written Opinion of the International Searching Authority, PCT/IB23/55312. ISA/US Commissioner for Patents, Alexandria, Virginia. Dated Aug. 30, 2023.
Written Opinion of the Searching Authority for PCT/IB2023/050848, dated May 9, 2023. International Bureau of WIPO.
Written Opinion of the Searching Authority of PCT/IB2023/058074, dated Nov. 20, 2023. Searching Authority United States Patent and Trademark Office, Alexandria, Virginia.
Zhang et al. BMC Bioinformatics 2014. “On finding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types”. http://www.biomedcentral.com/1471-2105/15/110.
Jordan, M. et al. Enabling pervasive encryption through IBM Z stack innovations. IBM Journal of Research and Development, vol. 62 Issue: 2/3, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp&arnumber=8270590 (Year: 2018).
Leibenger, Dominik et al. EncFS goes multi-user: Adding access control to an encrypted file system. 2016 IEEE Conference on Communications and Network Security (CNS). https://ieeexoplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7860544 (Year: 2016).
Sahil Suneja; Safe Inspection of Live Virtual Machines; IEEE; Year:2017; pp. 97-111.
Siqi Ma; Certified Copy? Understanding Security Risks of Wi-Fi Hotspot based Android Data Clone Services; ACM; Year: 2021; pp. 320-331.
Chang, Bing et al. MobiCeal: Towards Secure and Practical Plausibly Deniable Encryption on Mobile Devices. 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8416506 (Year: 2018).
Islam, Md Shihabul et al. Secure Real-Time Heterogeneous IoT Data Management System. 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). https:// ieeexplore.ieee.org/stamp/ stamp.jsp?tp=&arnumber=9014355 (Year: 2019).
Safaryan, Olga A et al. Cryptographic Algorithm Implementation for Data Encryption in DBMS MS SQL Server. 2020 IEEE East-West Design & Test Symposium (EWDTS). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9224775 (Year: 2020).
Wassermann, Sarah et al. ViCrypt to the Rescue: Real-Time, Machine-Learning-Driven Video-QoE Monitoring for Encrypted Streaming Traffic. IEEE Transactions on Network and Service Management, vol. 17, Issue: 4. https://ieeexplore.ieee.org/stamp/ stamp.jsp?tp=&arnumber=9250645 (Year: 2020).
Related Publications (1)
Number Date Country
20240119145 A1 Apr 2024 US
Continuations (1)
Number Date Country
Parent 18045046 Oct 2022 US
Child 18457017 US