International Business Machines Corporation

Kyndryl, Inc.

Douglas Lee Schales

Marc Philippe Stoecklin

Ting Wang

Xin Hu

Reiner Sailer

Mihai Christodorescu

Jiyong Jang

Josyula R Rao

Frederico Araujo

Teryl Paul Taylor

Marc Ph Stoecklin

Andrew Maxwell White

Xiaokui Shu

Dimitrios Pendarakis

Stefan Berger

Dhilung Hang Kirat

Marc Stoecklin

Yangyi Chen

Bum Jun Kwon

Ian M Molloy

Suresh N Chari

Lawrence H Koved

Srinivasan Parthasarathy

Taesung Lee

Lisa Amini

Kapil Kumar Singh

Pau-Chen Cheng

Mitchell Adam Cohen

Miriam Zohar

Kevin Eykholt

Wietse Z Venema

Oliver Verscheure

Srinivasan Seshan

Yushan Liu

Dimitrious Pendarakis

Najwa Aaraj

Dmytro Korzhyk

A processor-implemented method generates adversarial example objects. One or more processors represent an adversarial input generation process as a graph. The processor(s) explore the graph, such that a sequence of edges on the graph are explored. The processor(s) create, based on the exploring, an adversarial example object, and utilize the created adversarial example object to harden an existing process model against vulnerabilities.

Graph exploration framework for adversarial example generation

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.

Iterative constraint solving in abstract graph matching for cyber incident reasoning

A cloud based implemented method (and apparatus) includes receiving input data including bipartite graph data in a format of source MAC (Media Access Control) address data versus destination IP (Internet Protocol) data and timestamp information, and providing the input bipartite graph data into a first processing to detect malicious beaconing activities using a lockstep detection module on the input bipartite graph data, as executed in a cloud environment, to detect possible synchronized attacks against a targeted infrastructure.

Detecting malicious beaconing communities using lockstep detection and co-occurrence graph

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. The mapping is output for use by an IDPS for determining whether a threat has occurred to the resources in the computer environment.

Automatic categorization of IDPS signatures from multiple different IDPS systems

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.

Real-time alert reasoning and priority-based campaign discovery

A decoy filesystem that curtails data theft and ensures file integrity protection through deception is described. To protect a base filesystem, the approach herein involves transparently creating multiple levels of stacking to enable various protection features, namely, monitoring file accesses, hiding and redacting sensitive files with baits, and injecting decoys onto fake system views that are purveyed to untrusted subjects, all while maintaining a pristine state to legitimate processes. In one implementation, a kernel hot-patch is used to seamlessly integrate the new filesystem module into live and existing environments.

Integrity, theft protection and cyber deception using a deception-based filesystem

A method for improving a detection of beaconing activity includes receiving input data into a computer-implemented processing procedure at least one listing of at least one of time series data and candidate periods of potential beaconing activity. The input data is processed, to detect candidates of potential beaconing activity. By further evaluating the time series data using techniques used for evaluating an analog signal, the performance of detecting of potential beaconing activity is improved to eliminate false positive indications of beaconing activity and/or to provide indication of multiple interleaved periodicities of beaconing.

Methods and systems for improving beaconing detection algorithms

Decoy network ports and services are projected onto existing production workloads to facilitate cyber deception, without the need to modify production machines. The approach may be implemented in a production network that includes two segments. A production machine is reachable via the first segment, while a decoy machine that offers the network service expected from the production machine is reachable via the second segment. A deception router is configured in front of the two segments, and it is not visible on the link and network layers. The router inspects network traffic destined for the production machine. Based on a set of one or more conditions being met, the router determines whether to relay network packets to the production machine, or to redirect the packet to the decoy machine.

Cyber-deception using network port projection

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.

Multi-point causality tracking in cyber incident reasoning

Rapid deployments of application-level deceptions (i.e., booby traps) implant cyber deceptions into running legacy applications both on production and decoy systems. Once a booby trap is tripped, the affected code is moved into a decoy sandbox for further monitoring and forensics. To this end, this disclosure provides for unprivileged, lightweight application sandboxing to facilitate monitoring and analysis of attacks as they occur, all without the overhead of current state-of-the-art approaches. Preferably, the approach transparently moves the suspicious process to an embedded decoy sandbox, with no disruption of the application workflow (i.e., no process restart or reload). Further, the action of switching execution from the original operating environment to the sandbox preferably is triggered from within the running process.

Application-level sandboxing

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.

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.

Identifying command and control endpoint used by domain generation algorithm (DGA) malware

Growing community of inventors

Ardsley, NY, United States of America

Douglas Lee Schales