Hewlett-packard Development Company, L.P.

Other

Daniel Cameron Ellam

Adrian John Baldwin

Jonathan Griffin

Remy Husson

Gurchetan Grewal

James M Mann

Nelson Liang An Chang

David Plaquin

Joshua Serratelli Schiffman

Christopher Ian Dalton

Pierre Belgarric

In an example, an apparatus is described. The apparatus comprises processing circuitry comprising a control module. The control module determines whether a computing device communicatively coupled to the control module is in a specified state for executing a machine learning model controlled by a third party entity. In response to determining that the computing device is in the specified state, the control module is to send, to an attestation module in a data processing pipeline associated with the computing device, an indication that the computing device is in the specified state.

Controlling execution of machine learning models

An apparatus and method is described comprising: classifying service ticket data relating to a service request into a service topic, wherein the service ticket data is obtained from the service request relating to a device; determining, for the service request, an extent to which the service topic matches a telemetry data class, wherein the telemetry data class relates to activities at the device; and providing an output according to said determination.

Telemetry data

Examples associated with ransomware attack monitoring are described herein. One example includes a monitor module to monitor files stored on the system for sequences of file accesses that match a predefined pattern of file accesses. An investigation module is activated based on a sequence of file accesses that match the predefined pattern. The investigation module logs actions taken by processes to modify files. A reaction module pauses a set of processes operating on the system based on the logging performed by the investigation module, and resumes legitimate processes.

Ransomware attack monitoring

There is disclosed a method, computer program product and a system for regulating execution of a suspicious process, comprising determining a file system location of an executable file associated with the suspicious process, encrypting the file, and creating a wrapper for the file with the same file name and location as the file associated with the suspicious process.

Regulating execution

In some examples, a system determines a difference between a received time indication and a previous time indication, performs a modular arithmetic operation with respect to a first integer on the difference, and increments a count related to a first set associated with a first result of the modular arithmetic operation. The system compares respective counts associated with respective sets of a plurality of sets including the first set, wherein each set of the plurality of sets is associated with a different result of the modular arithmetic operation, and detects an occurrence of a security intrusion based on the comparison.

Activity detection based on time difference metrics

In an example, a method includes determining an operating environment of a device based on sensor data from a sensor of the device that senses surroundings of the device. Access to a resource may be controlled based on the operating environment and a status of a security feature of the device.

Controlling access to a resource based on the operating environment

In an example, a method includes analysing data collected from a service. A value representative of the number of anomalies in the data is generated, this value then being compared with a threshold. Depending on whether the value is greater or less than the threshold, a performance parameter of the monitoring service may be evaluated.

Evaluation of a performance parameter of a monitoring service

Examples associated with user authentication are described. One example system includes a set of authentication modules. A data store stores data describing disruption ratings of members of the set of authentication modules. A user confidence module maintains a confidence rating that a current user of a device is an authenticated user of the device. The confidence module controls execution of the authentication modules based on the confidence rating and on the disruption ratings of the authentication modules. The user confidence module controls execution of relatively less disruptive authentication modules when the user confidence module is confident that the current user of the device is the authenticated user of the device. The user confidence module maintains the confidence rating based on feedback received from authentication modules.

User authentication

Examples disclosed herein relate to determining whether to allow access to an IP address based on a security level and domain name access information. In one implementation, a processor intercepts a response to a Domain Name System request related to a domain name and updates a security level based on the request and/or response. The processor may determine whether to forward or block the response based on the security level and a comparison of the domain name to stored information related to previous domain name access.

IP address access based on security level and access history

In an example, a machine-readable medium includes instructions that, when executed by a processor, cause the processor to order, as part of an execution of a trusted process, a plurality of processes into a sequence comprising a first process, at least one intermediate process, and a last process. The machine-readable medium may further comprise instruction to cause the processor to generate, as part of an execution of the first process, a value based on a code portion of the process following the first process in the sequence, and to generate, as part of an execution of each intermediate process, a respective value based on the value generated by the process preceding the intermediate process in the sequence and based on a code portion associated with the process following the intermediate process in the sequence.

Executing processes in sequence

Examples associated with ransomware attack monitoring are described. One example includes a monitor module to monitor files stored on the system for sequences of file accesses that match a predefined pattern of file accesses. An investigation module is activated when a number of sequences of file accesses that match the predefined pattern exceeds a first threshold. The investigation module logs actions taken by processes to modify files. A reaction module pauses a set of processes operating on the system when the number of sequences of file accesses that match the predefined pattern exceeds a second threshold. The reaction module then identifies processes associated with a suspected ransomware attack based on the logging performed by the investigation module, and resumes legitimate processes.

Examples disclosed herein relate to detecting an activity based on time difference metrics. In one implementation, a processor determines metrics related to the differences between the sizes of sets. For example, each set may include time indications that result in the particular integer associated with the set when a modular operation is performed on a difference between the respective time indication and a first time indication. The processor may detect a pattern indicative of an activity based on the metrics and output an indication that the activity is detected.

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Bristol, United Kingdom

Daniel Cameron Ellam