Cisco Technology, Inc.

Andrew Zawadowskiy

Vincent E Parla

Jeffrey Albin Kraemer

Oleg Bessonov

Donovan O'Hara

David A McGrew

Daniel George Wing

Tomas Pevny

Saravanan Radhakrishnan

Philip J S Gladstone

Boris Ruchansky

Thomas Szigeti

Ashok Krishnaji Moghe

Antonio Martin

Todd Short

Mikhail Cherepov

Kyle Andrew Donald Mestery

Hendrikus G P Bosch

Philip John Steuart Gladstone

Christopher Fitzgerald

David James McCowan

Alok Mittal

David Mccgrew

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 method comprises receiving, at a network infrastructure device, a flow of packets, determining, using the network infrastructure device and for a first subset of the packets, that the first subset corresponds to a first datagram and determining a first length of the first datagram, determining, using the network infrastructure device and for a second subset of the packets, that the second subset corresponds to a second datagram that was received after the first datagram, and determining a second length of the second datagram, determining, using the network infrastructure device, a duration value between a first arrival time of the first datagram and a second arrival time of the second datagram, sending, to a collector device that is separate from the network infrastructure device, the first length, the second length, and the duration value for analysis.

Detection of malware and malicious applications

In one embodiment, a monitoring engine obtains mesh flow data for traffic flows between nodes in a service mesh. The monitoring engine associates the mesh flow data with network traffic between an endpoint device and an edge of the service mesh. The monitoring engine identifies, based on the mesh flow data, a particular container workload associated with the traffic flows. The monitoring engine provides an indication that the particular container workload is associated with the network traffic between the endpoint device and the edge of the service mesh.

Network flow attribution in service mesh environments

In one example embodiment, a computing device has a processor that executes a processor instruction stream that causes the processor to perform one or more operations for the computing device. The computing device generates one or more trace data packets including a first instruction pointer of the processor instruction stream, a second instruction pointer of the processor instruction stream subsequent to the first instruction pointer, and a string of characters derived from instructions associated with a control flow transfer between the first instruction pointer of the processor instruction stream and the second instruction pointer of the processor instruction stream. The computing device determines whether the one or more trace data packets are consistent with a secure processor instruction stream known or determined to be secure from malicious processor instructions and, if not, generates an indication that the processor instruction stream is not secure.

Monitoring real-time processor instruction stream execution

A method for providing authoritative application-based routing and an improved application firewall, as well as a method for application classification, is described. The first embodiment, which provides a method for authoritative application-based routing, comprises tagging packets with an application identifier, and pushing the tagged packets to the network to enable the application identifier to be used in routing and priority decisions. In the second embodiment, a method for improving application firewall comprises using the application identifier to minimize the amount of processing required by the firewall when analyzing packet information.

Method for providing authoritative application-based routing and an improved application firewall

In one embodiment, a method is provided for improving data center and endpoint network visibility and security. The method comprises detecting a communication flow of a plurality of packets over a network, and generating a flow identifier that uniquely identifies the communication flow. After determining an application associated with the communication flow, a flow record is generated. The flow record includes the flow identifier and an indication of the application associated with the communication flow. The indication of the application may be, for example, a hash of the application binary file.

Application identification in records of network flows

A system receives information from at least one security interceptor associated with at least one computer system. The information identifies details associated with a traffic flow in a computer system of the computer networking environment. The system determines a probability that an attack on the computer system is in progress based on a probabilistic link provided by the information. The probabilistic link is determined by attack information associated with previous attacks. Based on the information provided by the at least one security interceptor, the system generates a signature utilized to prevent a similar attack on the computer system.

Growing community of inventors

Hollis, NH, United States of America

Andrew Zawadowskiy