Abstract: A method to test an OS kernel interface, such as an eBPF helper function. The interface has a grammar that defines the kernel interface. Testing is carried out using eBPF code that invokes and tests the interface using a fuzzing engine. To facilitate the process, additional user space code is configured to generate at least one kernel event that triggers the eBPF code to run, and to transform inputs from the fuzzing engine according to the grammar that defines the kernel interface. After loading the eBPF code into the OS kernel, the user space code issues the kernel event that causes the eBPF code to run. In response, and as the fuzzing engine executes, the eBPF code records arguments sent to the OS kernel through the kernel interface. The arguments are passed through a data structure shared by the eBPF code and the user space code. By recording the arguments and other diagnostic information, the security of the kernel interface is evaluated.

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: 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.

Abstract: Program analysis is provided. An intermediate representation of a program is generated. A set of structured inputs is provided to the program. The set of structured inputs are derived from the intermediate representation. The program is executed using the set of structured inputs. A set of action steps is performed in response to observing a violation of a policy during execution of the program using the structured inputs.

Abstract: 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 stateful security policy, where the stateful security policy has connection to previous data. The method may also include determining that the stateful security policy applies to a corresponding container. The method may also include enforcing the stateful security policy against the container. The system and computer program product may include similar steps.

Abstract: 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—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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method, apparatus and computer program product for automated security policy synthesis and use in a container environment. In this approach, a binary analysis of a program associated with a container image is carried out within a binary analysis platform. During the binary analysis, the program is micro-executed directly inside the analysis platform to generate a graph that summarizes the program's expected interactions within the run-time container environment. The expected interactions are identified by analysis of one or more system calls and their arguments found during micro-executing the program. Once the graph is created, a security policy is then automatically synthesized from the graph and instantiated into the container environment. The policy embeds at least one system call argument. During run-time monitoring of an event sequence associated with the program executing in the container environment, an action is taken when the event sequence is determined to violate the security policy.

Abstract: Program analysis is provided. An intermediate representation of a program is generated. A set of structured inputs is provided to the program. The set of structured inputs are derived from the intermediate representation. The program is executed using the set of structured inputs. A set of action steps is performed in response to observing a violation of a policy during execution of the program using the structured inputs.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A neural network is trained using a training data set, resulting in a set of model weights, namely, a matrix X, corresponding to the trained network. The set of model weights is then modified to produce a locked matrix X?, which is generated by applying a key. In one embodiment, the key is a binary matrix {0, 1} that zeros (masks) out certain neurons in the network, thereby protecting the network. In another embodiment, the key comprises a matrix of sign values {?1, +1}. In yet another embodiment, the key comprises a set of real values. Preferably, the key is derived by applying a key derivation function to a secret value. The key is symmetric, such that the key used to protect the model weight matrix X (to generate the locked matrix) is also used to recover that matrix, and thus enable access to the model as it was trained.

Abstract: 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.

Abstract: 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.