Code 42 Software, Inc.

Crashplan Group LLC

Other

Daniel L Sullivan

Ajaykumar Rajasekharan

Matthew Mills Parker

Peter John Lindquist

Vijay Ramanathan

Christopher Michael Montgomery

Brent Aethon McCullough

Rob Juncker

Danaca Booth

A system for forensic file services is configured to receive data indicative of operations executed on a filesystem element stored on a computing resource associated with a first tenant, to and adjust the data according to an indicated database schema. The system is also configured to store the adjusted data in a record of a partition of a database, where the partition configured to store a history of operations executed on filesystem elements of computing resources associated with the first tenant. The system is further configured to receive a search request to search the database and to execute the search request on a second partition of the database to identify one or more matching records. The system is additionally configured to provide the identified records in response to the search request.

Forensic file service

Disclosed in some examples are systems, methods, and machine readable mediums for identifying insider threats by determining file system element activity models that correlate to undesirable behavior and then utilizing the determined model to detect insider threats. Events involving file system elements of a client computing device (e.g., a network endpoint) may be monitored by a file system element monitoring application on the client computing device. The values of these signals are aggregated across all events of the same type that have occurred within a predetermined time window (e.g., an hour) for a particular client computing device. Each time an aggregated signal has a value over the threshold, an anomaly is recorded. Anomaly counts for each signal are then calculated as the aggregate number of anomalies for a particular signal over a second time period, the span of which is determined by the generation of first anomaly to the close of an alert by the network monitor. The anomaly counts for the signals are then weighted and summed to produce a risk score.

User behavior analytics for insider threat detection

A method of detecting the onset of a ransomware attack is presented. In an example embodiment, file backup metadata for each of a plurality of computing devices is accessed and analyzed to detect anomalous file backup activity of individual ones of the computing devices. A determination is made as to whether the detected anomalous file backup activity of at least some of the computing devices is correlated in time. File description metadata for each of the computing devices is also accessed and analyzed to identify files in the computing devices that are anomalous to other files in the computing devices. A determination whether a ransomware attack has begun is based on a determination that the detected anomalous file backup activity of at least some of the computing devices is correlated in time, as well as on the identified anomalous files.

Ransomware attack onset detection

A system for detecting anomalous user interactions with a computing resource a processor and a memory communicatively coupled to the processor and configured with instructions, which cause the processor to perform operations including receiving a request to monitor interactions of a user with the computing resource, obtaining first event data first event data that includes information that is indicative of first interactions of the user with the computing resource prior to receiving the request and obtaining second event data that includes information that is indicative of second interactions of the user with the computing resource after receiving the request. The operations further include determining, based on the first event data and the second event data, whether a deviation between the first interactions and the second interactions satisfies an indicated criteria. The operations additionally include generating a security alert based on the determination.

Process for automated investigation of flagged users based upon previously collected data and automated observation on a go-forward basis

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Denver, CO, United States of America

Daniel L Sullivan