Cisco Technology, Inc.

Oleg Bessonov

Vincent E Parla

Andrew Zawadowskiy

Thomas Szigeti

Ashok Krishnaji Moghe

Blake Harrell Anderson

Hendrikus G P Bosch

Nancy Cam-Winget

Subharthi Paul

Sanjay Agarwal

Saman Taghavi Zargar

Robert Frederick Albach

Mark Steven Knellinger

Techniques and systems described herein relate to monitoring executions of computer instructions on computing devices based on learning and generating a control flow directed graph. The techniques and systems include determining a learned control flow directed graph for a process executed on the computing system. A system call is identified during execution of the process as well as a predetermined number of transitions leading to the system call. A validity of the transitions leading the system call is determined based on the learned control flow directed graph and the computing system may perform an action based on the validity.

Inline control flow monitor with enforcement

Techniques and systems described herein relate to monitoring executions of computer instructions on computing devices based on learning and generating a control flow directed graph. The techniques and systems include determining an observation phase for a process or application on a computing device. During the observation phase, CPU telemetry is determined and used to generate a control flow directed graph. After the control flow directed graph is generated, a monitoring phase may be entered where transfers of instruction pointers are monitored based on the control flow directed graph to identify invalid transfers.

Control flow integrity monitoring for applications running on platforms

Techniques and systems described herein relate to monitoring executions of computer instructions on computing devices based on learning and generating a control flow directed graph. The techniques and systems include determining telemetry representing execution of a process on a computing system and accessing a learned control flow diagram graph for the process. A transfer of an instruction pointer is determined based on the telemetry and a validity of the transfer is determined based on the learned control flow directed graph. If invalid, then an action to terminate the process is determined, otherwise the action may be allowed to execute when valid.

Control flow integrity enforcement for applications running on platforms

Techniques for auto tuning keepalive packets intervals to an optimal interval are described. A remote secure session between a client device and a server over a network is established. A determination is made to identify an optimal keepalive interval for sending packets to keep the remote secure session alive over the network, the optimal keepalive interval defining an amount of time between sending of packets that keep a connection open through middleboxes in the network. Keepalive test probes are transmitted by the client device and to the server at different time intervals. An optimal keepalive interval is determined based at least in part on the keepalive test probes transmitted at the different intervals. The client device transmits information indicating the optimal keepalive interval to the server. Finally, the client device transmits keepalive packets according to the optimal keepalive interval.

Autotuning optimal keepalive intervals for secure sessions

Techniques and systems described herein relate to monitoring executions of computer instructions on computing devices based on learning and generating a control flow directed graph. The techniques and systems include determining a learned control flow directed graph for executable code of an application by observing executions of transitions during an observation period and determining destinations of indirect transfers based on the learned control flow directed graph. Next a disassembly of the executable code is determined based on the learned control flow directed graph, the destinations of the transfers, and the executable code.

Control flow directed graph for use with program disassembler

A system and method are provided for generating a cybersecurity behavioral graph from a log files and/or other telemetry data, which can be unstructured or semi-structured data. The log files are applied to a machine learning (ML) model (e.g., a large language model (LLM)) that generates/extract from the log files entities and relationships between said entities. The entities and relationships can be constrained using a cybersecurity ontology or schema to ensure that the results are meaningful to a cybersecurity context. A graph is then generated by mapping the extracted entities to nodes in the graph and the relationships to edges connecting nodes. To more efficiently extract the entities and relationships from the data file, an LLM is used to generate regular expressions for the format of the log files. Once generated, the regular expressions can rapidly parse the log files to extract the entities and relationships.

System and method using a large language model (LLM) and/or regular expressions for feature extractions from unstructured or semi-structured data to generate ontological graph

A method of defining priority of a number of data packets within a queue includes generating a policy. The policy defines a first multiplexed channel of a plurality of multiplexed channels. The first multiplexed channel having a first priority. The policy also defines a second multiplexed channel of the plurality of multiplexed channels. The second multiplexed channel having a second priority. The first priority is defined as being of a higher priority relative to the second priority. The method further includes receiving the number of data packets over the plurality of multiplexed channels associated with a session based at least in part on the policy.

Data packet prioritization in multiplexed sessions

A trained model may be deployed to an Internet-of-Things (IOT) operational environment in order to ingest features and detect events extracted from network traffic. The model may be received and converted into a meta-language representation which is interpretable by a data plane engine. The converted model can then be deployed to the data plane and may extract features from network communications over the data plane. The extracted features may be fed to the deployed model in order to generate event classifications or device state classifications.

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San Jose, CA, United States of America

Oleg Bessonov