Abstract: An automated method for processing security events in association with a cybersecurity knowledge graph. The method begins upon receipt of information from a security system representing an offense. An initial offense context graph is built based in part on context data about the offense. The graph also activity nodes connected to a root node; at least one activity node includes an observable. The root node and its one or more activity nodes represent a context for the offense. The knowledge graph, and potentially other data sources, are then explored to further refine the initial graph to generate a refined graph that is then provided to an analyst for further review and analysis. Knowledge graph exploration involves locating the observables and their connections in the knowledge graph, determining that they are associated with known malicious entities, and then building subgraphs that are then merged into the initial graph.

Abstract: A computer-implemented method (and apparatus) includes receiving input data comprising bipartite graph data in a format of source MAC (Machine Access Code) data versus destination IP (Internet Protocol) data and timestamp information. The input bipartite graph data is provided into a first processing to detect malicious beaconing activities using a lockstep detection method on the input bipartite graph data to detect possible synchronized attacks against a targeted infrastructure. The input bipartite graph data is also provided into a second processing, the second processing initially converting the bipartite graph data into a co-occurrence graph format that indicates in a graph format how devices in the targeted infrastructure communicate with different external destination servers over time. The second processing detects malicious beaconing activities by analyzing data exchanges with the external destination servers to detect anomalies.

Abstract: A command endpoint used by Domain Generation Algorithm (DGA) malware is identified using machine learning-based clustering. According to this technique, at least one attribute associated with a candidate resolved DNS name is identified. The candidate resolved DNS name has associated therewith a set of names that are failed DNS lookups but that cluster with the candidate resolved DNS name. A set of additional names that share the at least one attribute with the candidate resolved DNS name are then identified. For the set of additional names, an extent to which the set of additional names also clusters with the set of names that are failed DNS lookups is then determined. The candidate resolved DNS name is characterized as associated with the command endpoint when the set of additional names cluster with the set of names that are failed DNS lookups to a configurable degree.

Abstract: A cognitive security analytics platform is enhanced by providing a technique for automatically inferring temporal relationship data for cybersecurity events. In operation, a description of a security event is received, typically as unstructured security content or data. Information such as temporal data or cues, are extracted from the description, along with security entity and relationship data. Extracted temporal information is processing according to a set of temporal markers (heuristics) to determine a time value marker (i.e., an established time) of the security event. This processing typically involves retrieval of information from one or more structured data sources. The established time is linked to the security entities and relationships. The resulting security event, as augmented with the identified temporal data, is then subjected to a management operation.

Abstract: A cognitive security analytics platform is enhanced by providing a computationally- and storage-efficient data mining technique to improve the confidence and support for one or more hypotheses presented to a security analyst. The approach herein enables the security analyst to more readily validate a hypothesis and thereby corroborate threat assertions to identify the true causes of a security offense or alert. The data mining technique is entirely automated but involves an efficient search strategy that significantly reduces the number of data queries to be made against a data store of historical data. To this end, the algorithm makes use of maliciousness information attached to each hypothesis, and it uses a confidence schema to sequentially test indicators of a given hypothesis to generate a rank-ordered (by confidence) list of hypotheses to be presented for analysis and response by the security analyst.

Abstract: An automated method for processing security event data in association with a cybersecurity knowledge graph having nodes and edges. It begins by receiving from a security system (e.g., a SIEM) information representing an offense. An offense context graph is built. Thereafter, and to enhance the offense context graph, given nodes and edges of the knowledge graph are prioritized for traversal based on an encoding captured from a security analyst workflow. This prioritization is defined in a set of weights associated to the graph nodes and edges, and these weights may be derived using machine learning. The offense context graph is then refined by traversing the nodes and edges of the knowledge graph according to a prioritization tailored at least in part by the encoding. In addition to using security analyst workflow to augment generation of weights, preferably the machine learning system provides recommendations back to the security analysts to thereby influence their workflow.

Abstract: A decoder deployed in one or more terminals, includes a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to receiving a noisy message and a noisy hash from the network, searching for a pair of matching candidates for the hash and message from two row spaces of noisy message vectors using a shared secret with an encoder, and outputting, by the decoder, a decoded message if the searching is successful.

Abstract: This disclosure provides for a signal flow analysis-based exploration of security knowledge represented in a graph structure comprising nodes and edges. “Conductance” values are associated to each of a set of edges. Each node has an associated “toxicity” value representing a degree of maliciousness associated with the node. The conductance value associated with an edge is a function of at least the toxicity values of the nodes to which the edge is incident. A signal flow analysis is conducted with respect to an input node representing an observable associated with an offense. The flow analysis seeks to identify a subset of the nodes that, based on their conductance values, are reached by flow of a signal representing a threat, wherein signal flow over a path in the graph continues until a signal threshold is met. Based on the analysis, nodes within the subset are designated as hypothesis nodes for further examination.

Abstract: An automated method for processing security events in association with a cybersecurity knowledge graph. The method begins upon receipt of information from a security system representing an offense. An initial offense context graph is built based in part on context data about the offense. The graph also activity nodes connected to a root node; at least one activity node includes an observable. The root node and its one or more activity nodes represent a context for the offense. The knowledge graph, and potentially other data sources, are then explored to further refine the initial graph to generate a refined graph that is then provided to an analyst for further review and analysis. Knowledge graph exploration involves locating the observables and their connections in the knowledge graph, determining that they are associated with known malicious entities, and then building subgraphs that are then merged into the initial graph.

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: Unknown and reference signatures are accessed. The unknown and reference signatures indicate patterns that correspond to known threats to resources (such as computer systems and/or computer networks) in a computer environment and comprise a multitude of descriptive elements having information describing different aspects of a corresponding signature. A set of similarity measures is created of the unknown and reference signatures from different perspectives, each perspective corresponding to a descriptive element. The set of similarity measures are integrated to generate an overall similarity metric. The overall similarity metric is used to find appropriate categories in the reference signatures into which the unknown signatures should be placed. The unknown signatures are placed into the appropriate categories to create a mapping from the unknown signatures to the reference signatures.

Abstract: A method includes receiving, at an input port of a computer, indication of HTTP (Hypertext Transfer Protocol) traffic and clustering, using a processor on the computer, the HTTP traffic according to a client IP (Internet Protocol) into a web session tree. A client tree structure of the web session tree is generated and the client tree structure is compared with tree structures of exploit kit samples.

Abstract: Approaches to deactivating evasive malware. In an approach, a computer system installs an imitating resource in the computer system and the imitating resource creates an imitating environment of malware analysis, wherein the imitating resource causes the evasive malware to respond to the imitating environment of the malware analysis as to a real environment of the malware analysis. In the imitating environment of malware analysis, the evasive malware determines not to perform malicious behavior. In another approach, a computer system intercepts a call from the evasive malware to a resource on the computer system and returns a virtual resource to the call, wherein in the virtual resource one or more values of the resource on the computer system are modified.

Abstract: An encoder includes a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generate a key, estimate a network capacity, and encode each bit of the key using a random matrix of a selected rank and the estimated network capacity for secure transmission of the key through a network.

Abstract: An encoder including a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to construct an encoded message using a message and a random element, construct a hash using a shared secret, and transmit the encoded message and the hash to a destination, through a network.

Abstract: An automated method for processing security events in association with a cybersecurity knowledge graph. The method begins upon receipt of information from a security system representing an offense. An initial offense context graph is built based in part on context data about the offense. The graph also activity nodes connected to a root node; at least one activity node includes an observable. The root node and its one or more activity nodes represent a context for the offense. The knowledge graph, and potentially other data sources, are then explored to further refine the initial graph to generate a refined graph that is then provided to an analyst for further review and analysis. Knowledge graph exploration involves locating the observables and their connections in the knowledge graph, determining that they are associated with known malicious entities, and then building subgraphs that are then merged into the initial graph.

Abstract: Unknown and reference signatures are accessed. The unknown and reference signatures indicate patterns that correspond to known threats to resources (such as computer systems and/or computer networks) in a computer environment and comprise a multitude of descriptive elements having information describing different aspects of a corresponding signature. A set of similarity measures is created of the unknown and reference signatures from different perspectives, each perspective corresponding to a descriptive element. The set of similarity measures are integrated to generate an overall similarity metric. The overall similarity metric is used to find appropriate categories in the reference signatures into which the unknown signatures should be placed. The unknown signatures are placed into the appropriate categories to create a mapping from the unknown signatures to the reference signatures.

Abstract: This disclosure provides for a signal flow analysis-based exploration of security knowledge represented in a graph structure comprising nodes and edges. “Conductance” values are associated to each of a set of edges. Each node has an associated “toxicity” value representing a degree of maliciousness associated with the node. The conductance value associated with an edge is a function of at least the toxicity values of the nodes to which the edge is incident. A signal flow analysis is conducted with respect to an input node representing an observable associated with an offense. The flow analysis seeks to identify a subset of the nodes that, based on their conductance values, are reached by flow of a signal representing a threat, wherein signal flow over a path in the graph continues until a signal threshold is met. Based on the analysis, nodes within the subset are designated as hypothesis nodes for further examination.

Abstract: A computer system trains an AI model to generate a key generated as a same key based on multiple different feature vectors, which are based on specified target environment attributes of a target environment domain. The computer system uses the key to encrypt concealed information as an encrypted payload and distributes the encrypted payload and the trained AI model to another computer system. The other computer system extracts environment attributes based on an environment domain accessible by the other computer system and decodes a candidate key by using the trained AI model that uses the extracted environment attributes of the domain environment as input. The trained AI model is trained to generate a key that is generated as a same key from multiple different feature vectors corresponding to specified target environment attributes of a target environment domain. The other computer system determines whether the candidate key is a correct key.

Abstract: An encoder including a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generating a message by aggregating a plurality of incoming packets, constructing an encoded message using the message and a random matrix, constructing of a hash using a shared secret, and transmitting the encoded message and the hash to a destination, through a network that performs network coding operations.