Sophos Limited

Andrew James Thomas

Kenneth D Ray

Mark David Harris

Russell Humphries

Daniel Salvatore Schiappa

Karl Ackerman

Andrew G P Smith

Simon Neil Reed

Beata Ladnai

Joseph H Levy

Daniel Stutz

Neil Robert Tyndale Watkiss

John Edward Tyrone Shaw

Robert Wendell Cook

Moritz Daniel Grimm

Harald Schütz

Ross McKerchar

Anthony John Merry

Shail R Talati

Dmitri Samosseiko

James Douglas Bean

Timothy Rayment

Mangal Rakesh Vankadaru

Prakash Kumar Talreja

Joshua Daniel Saxe

Andreas Berger

Erik Jan Loman

Jan Karl Heinrich Weber

Lute Edwin Engels

Biju Balakrishnan Nair

Anil Kaushik

Dirk Bolte

Biju Ramachandra Kaimal

Venkata Suresh Reddy Obulareddy

Fraser Howard

Sanjeev Maheve

Nikhil Bhandari

Richard Jacobs

Amit Katyal

Thiyagu Rajendran

James I G Lyne

David James Mitchell

Paul Stuart Murray

Kerav Vaidya

Robert Paul Andrews

Steven John Braggs

Robert William Allsworth

Rade Todorovic

Bradley A C Manring

Andrew Colin Piper

Yogesh Kumar Bansal

Avni Bhupendrakumar Wala

Howard Moore

Craig Paradis

Madan Mohan Nayak

Daniel Montaque Teal

Srisakthi Subramaniam

Chloe Bell

Mark Andrew Gill

Timothy Edward Cobley

Mario Magdic

Trevor Neil McGing

Calum Anders McKay Grant

Daphne Kyriaki Allamenou

Mark Anthony Russo

Thomas John Walsh

Christoph Georg Hoyer

Johan Petter Nordwall

Mirco Stratmann

Mayur Premi

Ramesh Kamath

Matthew Charles Setzer

Thomas Rolf-Werner Eckert

Andrew Gp Smith

Robert W Cook

A collection of documents or other files and the like within an enterprise network are labelled according to an enterprise document classification scheme, and then a recognition model such as a neural network or other machine learning model can be used to automatically label other files throughout the enterprise network. In this manner, documents and the like throughout an enterprise can be automatically identified and managed according to features such as confidentiality, sensitivity, security risk, business value, and so forth.

Enterprise document classification

A threat management facility stores a number of entity models that characterize reportable events from one or more entities. A stream of events from compute instances within an enterprise network can then be analyzed using these entity models to detect behavior that is inconsistent or anomalous for one or more of the entities that are currently active within the enterprise network.

Centralized event detection

An endpoint in an enterprise network is instrumented with sensors to detect security-related events occurring on the endpoint. Event data from these sensors is augmented with contextual information about, e.g., a source of each event in order to facilitate improved correlation, analysis, and visualization at a threat management facility for the enterprise network.

Data augmentation for threat investigation in an enterprise network

A computer-implemented method includes identifying one or more software processes that execute on the endpoint device and that perform at least one file operation including opening a file, reading the file, writing the file, or transmitting the file over a network. The method further includes storing for each software process of the one or more software processes identification information about the file. The method further includes responsive to determining that a triggering event has occurred, performing one or more actions including: preventing deletion of the file, determining one or more attributes of a suspicious process that accessed the file, requesting that a separate component analyze event journal records in relation to a time interval that overlaps with when the suspicious process accessed the file, or transmitting a cryptographic hash of the file to a server.

Monitoring process data acquisition and exfiltration

A platform for managing threat data integrates threat data from a variety of sources including internal threat data from instrumented compute instances associated with an enterprise network and threat data from one or more independent, external resources. Threat assessments are incrementally revised as this threat data is asynchronously received from various sources, and a threat intervention container is automatically created and presented to an investigator when a composite threat score for one or more of the compute instances meets a predetermined threshold.

Platform for managing threat data

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

An automated system attempts to characterize code as safe or unsafe. For intermediate code samples not placed with sufficient confidence in either category, human-readable analysis is automatically generated to assist a human reviewer in reaching a final disposition. For example, a random forest over human-interpretable features may be created and used to identify suspicious features in a manner that is understandable to, and actionable by, a human reviewer. Similarly, a k-nearest neighbor algorithm may be used to identify similar samples of known safe and unsafe code based on a model for, e.g., a file path, a URL, an executable, and so forth. Similar code may then be displayed (with other information) to a user for evaluation in a user interface. This comparative information can improve the speed and accuracy of human interventions by providing richer context for human review of potential threats.

Computer augmented threat evaluation

Disclosed herein is a technique for detecting potential phishing attacks by monitoring outbound web traffic from an endpoint, along with inbound electronic mail traffic addressed to a user of the endpoint. With this information, a search can be performed for possible sources in the web traffic of a request for a hyperlink located in the inbound mail traffic, and when no source is located, phishing remediation can be performed, including restrictions on access to the hyperlink at an endpoint operated by the user.

Detecting phishing attacks

Security is improved by adding a security heartbeat for and endpoint as a factor in a multi-factor authentication system. The security heartbeat may be used directly as an authentication factor, e.g., where the heartbeat provides a reliable and verifiable indication of identity, or the security heartbeat may be used as a gating input for some other verification method, e.g., where a text message with a temporary security code can only be transmitted to a user when the user's endpoint is providing a secure heartbeat.

Multi-factor authentication

An application executing on an endpoint accesses remote resources using a gateway. In response to a requested remote access, the application may be marked with a descriptor that specifies a target action and a pattern of occurrences of the target action. When a second observable action on the endpoint includes the pattern of events following the first observable action, a reportable event may be generated indicating a compromised state of the endpoint. The gateway can then regulate usage of the remote resource based on the reportable event.

Firewall techniques for colored objects on endpoints

A firewall uses information about an application that originates a network request to determine whether and how to forward the request over a network. The firewall may more generally rely on the identity of the originating application, the security state of the originating application, the security state of the endpoint, and any other information that might provide an indication of malicious activity, to make routing and forwarding decisions for endpoint-originated network traffic.

Application firewall

A threat management facility detects a device on an enterprise network and determines whether the device is one of a set of managed devices for the enterprise network. When the device is not one of the set of managed devices, the threat management facility may selectively direct the device to a portal that provides support to the user of the device while the device awaits admission to the enterprise network. As the user interacts with the portal, the portal may manage admission of unrecognized devices onto the enterprise network while making efficient use of network administrator resources.

Managing admission of unrecognized devices onto an enterprise network

In order to use zero trust network resources distributed across multiple gateways, an agent is deployed on an endpoint of an enterprise network. The agent maps requests for specific applications to corresponding gateways. The agent may also multiplex or otherwise aggregate communications among different network applications and gateways in order to provide seamless, transparent access to the distributed resources at a single endpoint, and/or within a single interface.

Distributed zero trust network access

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

A platform for threat investigation in an enterprise network receives threat data from managed endpoints, and is augmented with data from cloud computing platforms and other third-party resources. The resulting merged data set can be incrementally updated and used to automatically launch investigations at appropriate times.

Composite threat score

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Oxfordshire, United Kingdom

Andrew James Thomas