International Business Machines Corporation

Kyndryl, Inc.

Hcl Technologies Limited

Marc Philippe Stoecklin

Jiyong Jang

Douglas Lee Schales

Dhilung Hang Kirat

Xin Hu

Ting Wang

Teryl Paul Taylor

Frederico Araujo

Youngja Park

Xiaokui Shu

Jialong Zhang

Reiner Sailer

Josyula R Rao

Zhongshu Gu

Andreas Kind

Mihai Christodorescu

Heqing Huang

Fabian Newman Monrose

Dimitrios Pendarakis

Suresh N Chari

Kaushal Kiran Kapadia

Michail Vlachos

Francesco Fusco

William Alexander Bird

Stefan Berger

Suzanne Carol Deffeyes

Sulakshan Vajipayajula

Anil Kurmus

Bernhard Jansen

Matthias Neugschwandtner

Yangyi Chen

Anton Beitler

Bum Jun Kwon

Ashish Kundu

Ian M Molloy

Hani Talal Jamjoom

Lawrence H Koved

Kapil Kumar Singh

Pau-Chen Cheng

Ted A Habeck

Ruediger Rissmann

Preeti Ravindra

Dimitrious Pendarakis

Markus A Regli

Alexander Fong

Dmytro Korzhyk

Yushan Liu

Anne E Kohlbrenner

Xiaorui Pan

A method, system and apparatus for software intelligence as-a-service, including decomposing software into functional blocks to provide a software genome, building a representation of the software genome in a knowledge graph linking granularities of the functional blocks, and identifying issues with a target software based on the knowledge graph.

Software intelligence as-a-service

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

Decoy data is generated from regular data. A deep neural network, which has been trained with the regular data, is trained with the decoy data. The trained deep neural network, responsive to a client request comprising input data, is operated on the input data. Post-processing is performed using at least an output of the operated trained deep neural network to determine whether the input data is regular data or decoy data. One or more actions are performed based on a result of the performed post-processing.

Detecting adversarial attacks through decoy training

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. The approach operates to understand running behavior of programs, to classify them, and then enable detection of potential malicious behaviors.

Characterizing user behavior in a computer system by automated learning of intention embedded in a system-generated event graph

A stackable filesystem that transparently tracks process file writes for forensic analysis. The filesystem comprises a base filesystem, and an overlay filesystem. Processes see the union of the upper and lower filesystems, but process writes are only reflected in the overlay. By providing per-process views of the filesystem using this stackable approach, a forensic analyzer can record a process's file-based activity&#x2014;i.e., file creation, deletion, modification. These activities are then analyzed to identify indicators of compromise (IoCs). These indicators are then fed into a forensics analysis engine, which then quickly decides whether a subject (e.g., process, user) is malicious. If so, the system takes some proactive action to alert a proper authority, to quarantine the potential attack, or to provide other remediation. The approach enables forensic analysis without requiring file access mediation, or conducting system event-level collection and analysis, making it a lightweight, and non-intrusive solution.

Identification and extraction of key forensics indicators of compromise using subject-specific filesystem views

Malware is detected and mitigated by differentiating HTTP error generation patterns between errors generated by malware, and errors generated by benign users/software. In one embodiment, a malware detector system receives traffic that includes HTTP errors and successful HTTP requests. Error traffic and the successful request traffic are segmented for further analysis. The error traffic is supplied to a clustering component, which groups the errors, e.g., based on their URI pages and parameters. During clustering, various statistical features are extracted (as feature vectors) from one or more perspectives, namely, error provenance, error generation, and error recovery. The feature vectors are supplied to a classifier component, which is trained to distinguish malware-generated errors from benign errors. Once trained, the classifier takes an error cluster and its surrounding successful HTTP requests as inputs, and it produces a verdict on whether a particular cluster is malicious. The classifier output then drives an automated mitigation operation.

Detecting and mitigating malware by evaluating HTTP errors

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.

Fuzzy cyber detection pattern matching

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

USB traffic is intercepted between a USB device and a computer system. It is determined whether the USB device has previously had a policy associated with it as to whether USB traffic from the device should be blocked, allowed, or sanitized. In response to not having a previous policy for the USB device, a request is made for a user to be prompted to provide a policy of one of block, allow, or sanitize for the USB device. In response to a user-provided-policy, one of the following are performed: blocking the traffic, allowing the traffic, or sanitizing the traffic between the USB device and the computer system. Apparatus, methods, and computer program products are disclosed.

Protecting computer systems from malicious USB devices via a USB firewall

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

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.

Graphical temporal graph pattern editor

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. The approach herein provides for systematic modeling of inter-process behaviors for characterizing malicious or suspicious patterns among processes.

Endpoint inter-process activity extraction and pattern matching

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

Growing community of inventors

White Plains, NY, United States of America

Marc Philippe Stoecklin