International Business Machines Corporation

University of North Carolina at Chapel Hill

Hcl Technologies Limited

Teryl Paul Taylor

Frederico Araujo

Marc Philippe Stoecklin

Jiyong Jang

Douglas Lee Schales

Fabian Newman Monrose

Ting Wang

Xin Hu

Jialong Zhang

Nathan Otterness

Srinivas Krishnan

John E McHugh

Kevin Zachary Snow

Marc Phillipe Stoecklin

William Blair

Will Blair

Anne E Kohlbrenner

Xu Lin

Md Sajidul Islam Sajid

Methods and systems for grayware analysis include running a software application in a sandbox. Activity information is collected from the software application that represents actions performed by the software application within an environment of the sandbox. The collected activity information is matched to a grayware activity description to identify the software application as performing a grayware activity. A corrective action is performed on the software application.

Grayware analysis

An approach is disclosed that receives system events corresponding to event occurrences that occur at an information handling system. The system events are combined into a set of one or more coalesced events, wherein the combining is based on a root node associated with the system events. The coalesced events are then transmitted to a security information and event management (SIEM) system.

Modulation and regulation of system event monitoring streams

A method of detecting deceptive web activity is implemented in an intermediary located between a requesting client device, and a server that hosts a web application. Following a bootstrap phase used to generate a database of information identifying characteristics of clients, the method begins by receiving a page directed to the client from the server. The server injects an invisible DOM element having a set of style properties associated therewith, with one of the set of style properties assigned a random value, to generate a modified page, which is returned to the client. As the client interacts with the modified page, the intermediary tracks the device's styles and uses them to identify the client from information in the database. Once the device is identified, the intermediary then detects whether a spoofing attack has occurred. By leveraging the tracked styles, a spoofing attack on the DOM element's styles may also be detected.

Detecting web resources spoofing through stylistic fingerprints

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

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

An intrusion detection system (IDS) for a micro-services environment identifies attacks in substantially real-time and at a container-level. In this approach, behavior models are generated from container images using a binary analysis. A behavior model is a graph data structure having nodes and edges, wherein an edge represents a system call made by at least one process represented as a node in the graph data structure. The model is co-located with a running container, thereby enabling detection of anomalies as the container executes in a container environment on a hardware node. A per-container IDS function is instantiated by checking whether system call telemetry generated by an image's running container satisfies the associated behavior model that has been generated for the container image. If the telemetry indicates activity that deviates from the behavior model, an automated action is then initiated to attempt to address the attack, preferably while it is in progress.

Intrusion detection in micro-services through container telemetry and behavior modeling

A method, system, and computer program product for performing microservice-aware reference policy checking that accept stateful security policies. The method may include receiving a security policy for a container that is part of a microservice architecture. The method may also include obtaining a first effect graph of the security policy, resulting in a security model for the container. The method may also include identifying execution behavior of the container. The method may also include generating a second effect graph of the execution behavior of the container, where the generating includes summarizing operations and interactions between entities in the execution behavior and results in a behavioral model. The method may also include comparing the behavioral model to the security model. The method may also include determining whether the container has deviated from the security policy based on the comparing. The method may also include enforcing the security policy against the container.

Stateful microservice-aware intrusion detection

A stackable filesystem architecture that curtails data theft and ensures file integrity protection. In this architecture, processes are grouped into ranked filesystem views, or 'security domains.' Preferably, an order theory algorithm is utilized to determine a proper domain in which an application is run. In particular, a root domain provides a single view of the filesystem enabling transparent filesystem operations. Each security domain transparently creates multiple levels of stacking to protect the base filesystem, and to monitor file accesses without incurring significant performance overhead. By combining its layered architecture with view separation via security domains, the filesystem maintains data integrity and confidentiality.

Filesystem view separation for data confidentiality and integrity using lattice-based security domains

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

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

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

Mechanisms are provided for protecting a neural network model against model inversion attacks. The mechanisms generate a decoy dataset comprising decoy data for each class recognized by a neural network model. The mechanisms further configure the neural network model to generate a modified output based on the decoy dataset that directs a gradient of the modified output to the decoy dataset. The neural network model receives and process input data to generate an actual output. The neural network model modifies one or more actual elements of the actual output to be one or more corresponding modified elements of the modified output, and returns the one or more corresponding modified elements, instead of the one or more actual elements, to the source computing device.

Defending against model inversion attacks on neural networks

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.

Growing community of inventors

Danbury, CT, United States of America

Teryl Paul Taylor