Abstract: A technique for storage-efficient cyber incident reasoning by graph matching. The method begins with a graph pattern that comprises a set of elements with constraints and connections among them. A graph of constraint relations (GoC) in the graph pattern is derived. An activity graph representing activity data captured in association with a host machine is then obtained. In response to a query, one or more subgraphs of the activity graph that satisfy the graph pattern are then located and, in particular, by iteratively solving constraints in the graph pattern. In particular, a single element constraint is solved to generate a result, and that result is propagated to connected constraints in the graph of constraint relations. This process continues until all single element constraints have been evaluated, and all propagations have been performed. The subgraphs of the activity graph that result are then returned in response to a database query.

Abstract: A computer-implemented method according to one embodiment includes causing a search to be performed for data on at least one security endpoint and organizing information about the performed search into steps and variables. Security analytics are run on a dataset provided from the performed search, and based on results of the analytics, a response is invoked to protect a system that interacts with the analyzed dataset. A computer program product according to another embodiment includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method. A system according to another embodiment includes a processor, and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor. The logic is configured to perform the foregoing method.

Abstract: Mechanisms are provided to implement a neural flow attestation engine and perform computer model execution integrity verification based on neural flows. Input data is input to a trained computer model that includes a plurality of layers of neurons. The neural flow attestation engine records, for a set of input data instances in the input data, an output class generated by the trained computer model and a neural flow through the plurality of layers of neurons to thereby generate recorded neural flows. The trained computer model is deployed to a computing platform, and the neural flow attestation engine verifies the execution integrity of the deployed trained computer model based on a runtime neural flow of the deployed trained computer model and the recorded neural flows.

Abstract: An automated technique for security monitoring leverages a labeled semi-directed temporal graph derived from system-generated events. The temporal graph is mined to derive process-centric subgraphs, with each subgraph consisting of events related to a process. The subgraphs are then processed to identify atomic operations shared by the processes, wherein an atomic operation comprises a sequence of system-generated events that provide an objective context of interest. The temporal graph is then reconstructed by substituting the identified atomic operations derived from the subgraphs for the edges in the original temporal graph, thereby generating a reconstructed temporal graph. Using graph embedding, the reconstructed graph is converted into a representation suitable for further machine learning, e.g., using a deep neural network. The network is then trained to learn the intention underlying the temporal graph.

Abstract: Mechanisms are provided to implement a neural flow attestation engine and perform computer model execution integrity verification based on neural flows. Input data is input to a trained computer model that includes a plurality of layers of neurons. The neural flow attestation engine records, for a set of input data instances in the input data, an output class generated by the trained computer model and a neural flow through the plurality of layers of neurons to thereby generate recorded neural flows. The trained computer model is deployed to a computing platform, and the neural flow attestation engine verifies the execution integrity of the deployed trained computer model based on a runtime neural flow of the deployed trained computer model and the recorded neural flows.

Abstract: A method to detect anomalous behavior in a computing system begins by training a graph neural network (GNN) in an unsupervised manner by applying contrastive representation learning on sets of positive samples and negative samples derived from one or more heterogeneous graphs using meta-path sampling. Following training, a temporal graph derived from system-generated events is received. The GNN is used to embed the temporal graph into a vector representation in a vector space. The trained GNN is also used to embed a set of attack pattern graphs into corresponding vector representations in the vector space. For anomaly detection, the representation corresponding to the temporal graph is compared to the representations corresponding to the attack pattern graphs. In one embodiment, the comparison is implemented using a fuzzy pattern matching algorithm. If a fuzzy match is found, an indication that the temporal graph is associated with a potential attack on the computing system is then output.

Abstract: Mechanisms for identifying a pattern of computing resource activity of interest, in activity data characterizing activities of computer system elements, are provided. A temporal graph of the activity data is generated and a filter is applied to the temporal graph to generate one or more first vector representations, each characterizing nodes and edges within a moving window defined by the filter. The filter is applied to a pattern graph representing a pattern of entities and events indicative of the pattern of interest, to generate a second vector representation. The second vector representation is compared to the one or more first vector representations to identify one or more nearby vectors, and one or more corresponding subgraph instances are output to an intelligence console computing system as inexact matches of the temporal graph.

Abstract: Advanced Persistent Threat (APT) defense leverages priority-based tracking around alerts, together with priority-based alert reasoning task scheduling. In one embodiment, individual alert reasoning tasks are managed by an alert scheduler, which effectively allocates available computation resources to prioritize the alert reasoning tasks, which each execute within processing workers that are controlled by the alert scheduler. An alert reasoning task typically is prioritized (relative to other such tasks) according to one or more factors, such as severity levels, elapsed time, and other tracking results. By implementing priority-based task scheduling, the task scheduler provides for alert reasoning tasks that are interruptible. In this approach, and once an alert is assigned to a task and the task assigned a worker, priority-based connectivity tracker around each alert is carried out to provide further computational efficiency.

Abstract: An interactive display system enables a user to compose a graph pattern for a temporal graph on a display screen. The system comprises a canvas that provides an interactive editing surface. The editor receives an input a set of user interactions, such as the drawing of lines and boxes, the specifying of attributes, and the like, that together compose a graph pattern. During the graph pattern composition, the user may retrieve other graph patterns (e.g., from a data store) and integrate them into the pattern being composed. Once the graph pattern is composed (or as it is being composed), the system converts the graphical pattern into a text-based representation, such as a computer program in a particular graph programming language, which is then used for subsequent processing and matching in a cybersecurity threat discovery workflow. The pattern (program code) also is stored to disk, from which it may be retrieved and converted back into its graphical view on the screen, e.g., for further editing and revision.

Abstract: An automated method for cyberattack detection and prevention in an endpoint. The technique monitors and protects the endpoint by recording inter-process events, creating an inter-process activity graph based on the recorded inter-process events, matching the inter-process activity (as represented in the activity graph) against known malicious or suspicious behavior (as embodied in a set of one or more pattern graphs), and performing a post-detection operation in response to a match between an inter-process activity and a known malicious or suspicious behavior pattern. Preferably, matching involves matching a subgraph in the activity graph with a known malicious or suspicious behavior pattern as represented in the pattern graph. During this processing, preferably both direct and indirect inter-process activities at the endpoint (or across a set of endpoints) are compared to the known behavior patterns.

Abstract: A computer-implemented method, a computer program product, and a computer system. The computer system installs and configures a virtual imitating resource in the computer system, wherein the virtual imitating resource imitates a set of resources in the computer system. Installing and configuring the virtual imitating resource includes modifying respective values of an installed version of the virtual imitating resource for an environment of the computer system, determining whether the virtual imitating resource is a static imitating resource or a dynamic imitating resource, and comparing a call graph of the evasive malware with patterns of dynamic imitating resources on a database. The computer system returns a response from an appropriate element of the virtual imitating resource, in response to a call from the evasive malware to a real computing resource, return, by the computer system.

Abstract: Mechanisms for identifying a pattern of computing resource activity of interest, in activity data characterizing activities of computer system elements, are provided. A temporal graph of the activity data is generated and a filter is applied to the temporal graph to generate one or more first vector representations, each characterizing nodes and edges within a moving window defined by the filter. The filter is applied to a pattern graph representing a pattern of entities and events indicative of the pattern of interest, to generate a second vector representation. The second vector representation is compared to the one or more first vector representations to identify one or more nearby vectors, and one or more corresponding subgraph instances are output to an intelligence console computing system as inexact matches of the temporal graph.

Abstract: Mechanisms are provided to implement a neural flow attestation engine and perform computer model execution integrity verification based on neural flows. Input data is input to a trained computer model that includes a plurality of layers of neurons. The neural flow attestation engine records, for a set of input data instances in the input data, an output class generated by the trained computer model and a neural flow through the plurality of layers of neurons to thereby generate recorded neural flows. The trained computer model is deployed to a computing platform, and the neural flow attestation engine verifies the execution integrity of the deployed trained computer model based on a runtime neural flow of the deployed trained computer model and the recorded neural flows.

Abstract: An interactive display system enables a user to compose a graph pattern for a temporal graph on a display screen. The system comprises a canvas that provides an interactive editing surface. The editor receives an input a set of user interactions, such as the drawing of lines and boxes, the specifying of attributes, and the like, that together compose a graph pattern. During the graph pattern composition, the user may retrieve other graph patterns (e.g., from a data store) and integrate them into the pattern being composed. Once the graph pattern is composed (or as it is being composed), the system converts the graphical pattern into a text-based representation, such as a computer program in a particular graph programming language, which is then used for subsequent processing and matching in a cybersecurity threat discovery workflow. The pattern (program code) also is stored to disk, from which it may be retrieved and converted back into its graphical view on the screen, e.g., for further editing and revision.

Abstract: A method to detect anomalous behavior in an execution environment. A set of system events captured from a monitored computing system are received. Using the received system events, a model is then trained using machine learning. The model is trained to automatically extract one or more features for the received set of system events, wherein a system event feature is determined by a semantic analysis and represents a semantic relationship between or among a grouping of system events that are observed to co-occur in an observation sample. An observation sample is associated with an operating scenario that has occurred in the execution environment. Once trained, and using the features, the model is used to detect anomalous behavior. As an optimization, prior to training, the set of system events are pre-processed into a reduced set of system events. The modeler may comprise a component of a malware detection system.

Abstract: An automated technique for security monitoring leverages a labeled semi-directed temporal graph derived from system-generated events. The temporal graph is mined to derive process-centric subgraphs, with each subgraph consisting of events related to a process. The subgraphs are then processed to identify atomic operations shared by the processes, wherein an atomic operation comprises a sequence of system-generated events that provide an objective context of interest. The temporal graph is then reconstructed by substituting the identified atomic operations derived from the subgraphs for the edges in the original temporal graph, thereby generating a reconstructed temporal graph. Using graph embedding, the reconstructed graph is converted into a representation suitable for further machine learning, e.g., using a deep neural network. The network is then trained to learn the intention underlying the temporal graph.

Abstract: This disclosure provides an automatic causality tracking system that meets real-time analysis needs. It solves causality tracking for cybersecurity, preferably as three sub-tasks: backward tracking, forward tracking, and path-finding. Given a set of threat indicators, the first sub-task yields the system elements (e.g., entities such as processes, files, network sockets, and the like) that contribute information to a set of threat indicators backward in time. The second sub-task yields system elements forward in time. Given two sets of threat indicators, the third sub-task yields shortest paths between them, e.g., how the two sets of indicators are connected to one another. The system enables efficient multi-point traversal analysis with respect to a set of potential compromise points, and using data from real information flows.

Abstract: Advanced Persistent Threat (APT) defense leverages priority-based tracking around alerts, together with priority-based alert reasoning task scheduling. In one embodiment, individual alert reasoning tasks are managed by an alert scheduler, which effectively allocates available computation resources to prioritize the alert reasoning tasks, which each execute within processing workers that are controlled by the alert scheduler. An alert reasoning task typically is prioritized (relative to other such tasks) according to one or more factors, such as severity levels, elapsed time, and other tracking results. By implementing priority-based task scheduling, the task scheduler provides for alert reasoning tasks that are interruptible. In this approach, and once an alert is assigned to a task and the task assigned a worker, priority-based connectivity tracker around each alert is carried out to provide further computational efficiency.

Abstract: This disclosure provides an automatic causality tracking system that meets real-time analysis needs. It solves causality tracking for cybersecurity, preferably as three sub-tasks: backward tracking, forward tracking, and path-finding. Given a set of threat indicators, the first sub-task yields the system elements (e.g., entities such as processes, files, network sockets, and the like) that contribute information to a set of threat indicators backward in time. The second sub-task yields system elements forward in time. Given two sets of threat indicators, the third sub-task yields shortest paths between them, e.g., how the two sets of indicators are connected to one another. The system enables efficient multi-point traversal analysis with respect to a set of potential compromise points, and using data from real information flows.

Abstract: A webpage navigation of a user over a timeframe and a second webpage navigation of a second user over a second timeframe may be received. A time-variant variable-order Markov model, comprising a context tree, may be generated utilizing the webpage navigation and the second webpage navigation. A third webpage navigation of a third user may be received. A probability that the third user may interact with content, that the third user is a non-human entity, and/or that the third user will access a website may be determined based upon an evaluation of the third webpage navigation using the time-variant variable-order Markov model. A second client device is instructed to present the content to the third user, to present a human verification mechanism to the third user, and/or to instruct a server, providing the website, to alter a server capacity for the website.