Sophos Limited

Mcafee, Inc.

Networks Associates Technology, Inc.

Mark David Harris

Andrew James Thomas

Kenneth D Ray

Russell Humphries

Andrew G P Smith

Beata Ladnai

Neil Robert Tyndale Watkiss

Simon Neil Reed

Robert Wendell Cook

Dmitri Samosseiko

Harald Schütz

Karl Ackerman

Daniel Stutz

Daniel Salvatore Schiappa

Paul Nicholas Gartside

Andreas Berger

John Edward Tyrone Shaw

Anthony John Merry

Richard Paul Cosgrove

Emile Marcus Kenning

Vincent Kevin Lynch

Igor G Muttik

Daniel Joseph Wolff

Fraser Peter Howard

Alex J Hinchliffe

Christopher Scott Bolin

James I G Lyne

Michael Kenneth Glover

Mario Magdic

David Emm

Andrew Gp Smith

In a threat management platform, a number of endpoints log events in an event data recorder. A local agent filters this data and feeds a filtered data stream to a central threat management facility. The central threat management facility can locally or globally tune filtering by local agents based on the current data stream, and can query local event data recorders for additional information where necessary or helpful in threat detection or forensic analysis. The central threat management facility also stores and deploys a number of security tools such as a web-based user interface supported by machine learning models to identify potential threats requiring human intervention and other models to provide human-readable context for evaluating potential threats.

Enterprise network threat detection

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An event graph can be generated, and, upon malware detection, traversed backward to identify a root cause associated with the malware detection. Using this information, rules for earlier malware detection can be created by analyzing the event graph proximal to the root cause rather than proximal to the malware detection trigger.

Tracking malware root causes with an event graph

An event graph associated with a root cause for a change in security state on an endpoint is used to facilitate malware detection on other endpoints.

An endpoint coupled in a communicating relationship with an enterprise network may include a data recorder configured to store an event stream of data indicating events on the endpoint including types of changes to computing objects, a filter configured to locally process the event stream into a filtered event stream including a subset of types of changes to the computing objects, and a local security agent. The local security agent may be configured to transmit the filtered event stream to a threat management facility, respond to a filter adjustment from the threat management facility by adjusting the filter to modify the subset of types of changes included in the filtered event stream, and respond to a query from the threat management facility by retrieving data stored in the data recorder over a time window before the query and excluded from the filtered event stream.

Endpoint with remotely programmable data recorder

A data recorder stores endpoint activity on an ongoing basis as sequences of events that causally relate computer objects such as processes and files. When a security event is detected, an event graph may be generated based on these causal relationships among the computing objects. For a root cause analysis, the event graph may be traversed in a reverse order from the point of an identified security event (e.g., a malware detection event) to preceding computing objects, while applying one or more cause identification rules to identify a root cause of the security event. Once a root cause is identified, the event graph may be traversed forward from the root cause to identify other computing objects that are potentially compromised by the root cause.

Forensic analysis of computing activity

A gateway or other network device may be configured to monitor endpoint behavior, and to request a verification of user presence at the endpoint under certain conditions suggesting, e.g., malware or other endpoint compromise. For example, when a network request is directed to a low-reputation or unknown network address, user presence may be verified to ensure that this action was initiated by a human user rather than automatically by malware or the like. User verification may be implicit, based on local behavior such as keyboard or mouse activity, or the user verification may be explicit, such as where a notification is presented on a display of the endpoint requesting user confirmation to proceed.

Network security

An endpoint in an enterprise network is monitored, and when a potential trigger for a distributed denial of service (DDoS) attack is followed by an increase in network traffic from the endpoint to a high reputation network address, the endpoint is treated as a DDoS service bot and isolated from the network until remediation can be performed.

Detecting triggering events for distributed denial of service attacks

A honeypot file is cryptographically secured with a cryptographic key. The key, or related key material, is then placed on a central keystore and the file is placed on a data store within the enterprise network. Unauthorized access to the honeypot file can then be detecting by monitoring use of the associated key material, which usefully facilitates detection of file access at any time when, and from any location where, cryptographic access to the file is initiated.

Intrusion detection with honeypot keys

A multi-endpoint event graph is used to detect malware based on malicious software moving through a network.

Tracking malicious software movement with an event graph

Activity on an endpoint is monitored in two stages with a local agent. In a first stage, particular computing objects on the endpoint are selected for tracking. In a second stage, particular types of changes to those objects are selected. By selecting objects and object changes in this manner, a compact data stream of information highly relevant to threat detection can be provided from an endpoint to a central threat management facility. At the same time, a local data recorder creates a local record of a wider range of objects and changes. The system may support forensic activity by facilitating queries to the local data recorder on the endpoint to retrieve more complete records of local activity when the compact data stream does not adequately characterize a particular context.

Forensic query of local event streams in an enterprise network

An enterprise security system is improved by instrumenting endpoints to explicitly label network flows according to sources of network traffic. When a network message from an endpoint is received at a gateway, firewall, or other network device/service, the network message may be examined to determine the application on the endpoint that originated the request, and this source information may be used to control routing or other handling of the network message.

Labeling network flows according to source applications

A security driver loads early in the boot process for a compute instance and detects processes that are subsequently launched. The detected processes can be recorded, and then scanned with any suitable malware scanning tool(s) once a user mode is available on the compute instance. After the operating system is installed and a user mode is available, other scanning tools may also be deployed (e.g., in the user mode) to augment security of the compute instance.

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Oxon, United Kingdom

Mark David Harris