Rapid7, Inc.

Katherine Wilbur

Roy Donald Hodgman

Vasudha Shivamoggi

Matthew Berninger

Luke Ludington

Jacqueline Daniel

Lauren Johnson

Some embodiments provide techniques for detecting cyberattacks against a software service authentication system that authorizes access to software services. The techniques access a user activity profile specifying values of parameters indicating the user's pattern of requesting access to unique software service(s). The techniques monitor the activity of the user over a time period to obtain software request data indicating request(s) by the user to access software services in the time period. The techniques determine, using the software service request data and the user activity profile, whether computing activity of the user during the time period is anomalous.

Techniques for detecting cyberattacks on an authentication system

A method includes obtaining a command captured at a computing device to start a process on the computing device submitted via a command line interface. The command is of a plurality of commands captured at respective computing devices that triggered respective alerts to review the plurality of commands. The method includes parsing the command to generate a plurality of tokens that represent the command according to dictionary of features of commands submitted via the command line interface, generating a feature vector based, at least in part, on the plurality of tokens, applying a classification model, trained on other commands submitted via the command line interface to predict benign commands, to the feature vector to determine a score indicative of a probability that the command is benign, and, responsive to a determination that the score is above a confidence threshold, removing the command from the plurality of commands to be reviewed.

Command line parsing for classification of process start alerts

Systems and methods are disclosed to implement a cyberattack detection system that monitors a computer network for lateral movement. In embodiments, the system uses network data from a computer network to build a baseline of connection behaviors for the network. Connection graphs are generated from new network data that indicate groups of nodes that made connections with one another during a last time interval. The graphs are analyzed for connection behavior anomalies and ranked to determine a subset of graphs with suspected lateral movement. Graphs with suspected lateral movement may be further analyzed to determine a set of possible attack paths in the lateral movements. The suspected attack paths are reported to network administrators via a notification interface. Advantageously, the disclosed system is able to detect potential lateral movements in localized portions of a network by monitoring for connection behavior anomalies in network data gathered from the network.

Attack monitoring service that selectively analyzes connection graphs for suspected attack paths

Graph-based detection of lateral movement in computer networks

Growing community of inventors

Brookline, MA, United States of America

Katherine Wilbur