Abstract: Systems and methods are described herein for computer user authentication using machine learning. Authentication for a user is initiated based on an identification confidence score of the user. The identification confidence score is based on one or more characteristics of the user. Using a machine learning model for the user, user activity of the user is monitored for anomalous activity to generate first data. Based on the monitoring, differences between the first data and historical utilization data for the user determine whether the user's utilization of the one or more resources is anomalous. When the user's utilization of the one or more resource is anomalous, the user's access to the one or more resource is removed.

Abstract: Bayesian continuous user authentication can be obtained by receiving observed behavior data that collectively characterizes interaction of an active user with at least one computing device or software application. A sequence of events within the observed behavior data can be identified and scored using a universal background model that generates first scores that characterize an extent to which each event or history of events is anomalous for a particular population of users. Further, the events are scored using a user model that generates second scores that characterizes an extent to which each event or history of events is anomalous for the particular user who owns the account. The first scores and the second scores are smoothed using a smoothing function. A probability that the active user is the account owner associated with the user model is determined based on the smoothed first scores and the smoothed second scores.

Abstract: Bayesian continuous user authentication can be obtained by receiving observed behavior data that collectively characterizes interaction of an active user with at least one computing device or software application. A sequence of events within the observed behavior data can be identified and scored using a universal background model that generates first scores that characterize an extent to which each event or history of events is anomalous for a particular population of users. Further, the events are scored using a user model that generates second scores that characterizes an extent to which each event or history of events is anomalous for the particular user who owns the account. The first scores and the second scores are smoothed using a smoothing function. A probability that the active user is the account owner associated with the user model is determined based on the smoothed first scores and the smoothed second scores.

Abstract: Systems and methods are described herein for computer user authentication using machine learning. Authentication for a user is initiated based on an identification confidence score of the user. The identification confidence score is based on one or more characteristics of the user. Using a machine learning model for the user, user activity of the user is monitored for anomalous activity to generate first data. Based on the monitoring, differences between the first data and historical utilization data for the user determine whether the user's utilization of the one or more resources is anomalous. When the user's utilization of the one or more resource is anomalous, the user's access to the one or more resource is removed.

Abstract: Systems and methods are described herein for computer user authentication using machine learning. Authentication for a user is initiated based on an identification confidence score of the user. The identification confidence score is based on one or more characteristics of the user. Using a machine learning model for the user, user activity of the user is monitored for anomalous activity to generate first data. Based on the monitoring, differences between the first data and historical utilization data for the user determine whether the user's utilization of the one or more resources is anomalous. When the user's utilization of the one or more resource is anomalous, the user's access to the one or more resource is removed.

Abstract: In one aspect, a computer-implemented method is disclosed. The computer-implemented method may include determining a sketch matrix that approximates a matrix representative of a reference dataset. The reference dataset may include at least one computer program having a predetermined classification. A reduced dimension representation of the reference dataset may be generated based at least on the sketch matrix. The reduced dimension representation may have a fewer quantity of features than the reference dataset. A target computer program may be classified based on the reduced dimension representation. The target computer program may be classified to determine whether the target computer program is malicious. Related systems and articles of manufacture, including computer program products, are also disclosed.

Abstract: In one aspect, a computer-implemented method is disclosed. The computer-implemented method may include determining a sketch matrix that approximates a matrix representative of a reference dataset. The reference dataset may include at least one computer program having a predetermined classification. A reduced dimension representation of the reference dataset may be generated based at least on the sketch matrix. The reduced dimension representation may have a fewer quantity of features than the reference dataset. A target computer program may be classified based on the reduced dimension representation. The target computer program may be classified to determine whether the target computer program is malicious. Related systems and articles of manufacture, including computer program products, are also disclosed.