Cylance Inc.

Michael Thomas Wojnowicz

Matthew Wolff

Andrew Davis

Derek A Soeder

Xuan Zhao

Ryan Permeh

John Brock

David Neill Beveridge

Jian Luan

Brian Michael Wallace

Mahdi Azarafrooz

Andy Wortman

Yaroslav Oliinyk

Dinh Huu Nguyen

Eric G Petersen

Glenn Chisholm

Ming Jin

Garret Florian Grajek

Jeffrey Lo

Aditya Kapoor

Homer Valentine Strong

Gabriel Acevedo

Matthew Maisel

Kevin Beets

Michael Alan Slawinski

Andrew Wortman

Alexander Wolfe Kohn

Brian Wallace

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.

Computer user authentication using machine learning

A system is provided for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.

Machine learning model for analysis of instruction sequences

Features are extracted from an artifact so that a vector can be populated. The vector is then inputted into an anomaly detection model comprising a deep generative model to generate a first score. The first score can characterize the artifact as being malicious or benign to access, execute, or continue to execute. In addition, the vector is inputted into a machine learning-based classification model to generate a second score. The second score can also characterize the artifact as being malicious or benign to access, execute, or continue to execute. The second score is then modified based on the first score to result in a final score. The final score can then be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Detecting malware with deep generative models

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.

Bayesian continuous user authentication

A system is provided for training a machine learning model to detect malicious container files. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one processor provides operations including: processing a container file with a trained machine learning model, wherein the trained machine learning is trained to determine a classification for the container file indicative of whether the container file includes at least one file rendering the container file malicious; and providing, as an output by the trained machine learning model, an indication of whether the container file includes the at least one file rendering the container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

Container file analysis using machine learning model

In one respect, there is provided a system for training a neural network adapted for classifying one or more scripts. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: reducing a dimensionality of a plurality of features representative of a file set; determining, based at least on a reduced dimensional representation of the file set, a distance between a file and the file set; and determining, based at least on the distance between the file and the file set, a classification for the file. Related methods and articles of manufacture, including computer program products, are also provided.

Anomaly based malware detection

In one respect, there is provided a system for training a machine learning model to detect malicious container files. The system may include at least one processor and at least one memory. The at least one memory may include program code that provides operations when executed by the at least one processor. The operations may include: training, based on a training data, a machine learning model to enable the machine learning model to determine whether at least one container file includes at least one file rendering the at least one container file malicious; and providing the trained machine learning model to enable the determination of whether the at least one container file includes at least one file rendering the at least one container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

Training a machine learning model for container file analysis

In one respect, there is provided a system for classifying malware. The system may include a data processor and a memory. The memory may include program code that provides operations when executed by the processor. The operations may include: providing, to a display, contextual information associated with a file to at least enable a classification of the file, when a malware classifier is unable to classify the file; receiving, in response to the providing of the contextual information, the classification of the file; and updating, based at least on the received classification of the file, the malware classifier to enable the malware classifier to classify the file. Methods and articles of manufacture, including computer program products, are also provided.

Advanced malware classification

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.

Dimensionality reduction of computer programs

In one respect, there is provided a system for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.

In one respect, there is provided a system for training a neural network adapted for classifying one or more instruction sequences. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one processor provides operations including: training, based at least on training data, a machine learning model to detect one or more predetermined interdependencies amongst a plurality of tokens in the training data; and providing the trained machine learning model to enable classification of one or more instruction sequences. Related methods and articles of manufacture, including computer program products, are also provided.

Training a machine learning model for analysis of instruction sequences

In one respect, there is provided a system for training a machine learning model to detect malicious container files. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one processor provides operations including: processing a container file with a trained machine learning model, wherein the trained machine learning is trained to determine a classification for the container file indicative of whether the container file includes at least one file rendering the container file malicious; and providing, as an output by the trained machine learning model, an indication of whether the container file includes the at least one file rendering the container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

Growing community of inventors

Irvine, CA, United States of America

Michael Thomas Wojnowicz