Microsoft Technology Licensing, LLC

Rakshit Agrawal

Adrian Mihail Marinescu

Jack Wilson Stokes, Iii

Karthik Selvaraj

Jack Wilson Stokes Iii

Jack Wilson Iii Stokes

Implementations described herein disclose a malware sequence detection system for detecting presence of malware in a plurality of events. An implementation of the malware sequence detection includes receiving a sequence of a plurality of events, and detecting presence of a sequence of malware commands within the sequence of a plurality of events by dividing the sequence of plurality of events into a plurality of subsequences, performing sequential subsequence learning on one or more of the plurality of subsequences, and generating a probability of one or more of the plurality of subsequences being a malware based on the output of the sequential subsequence.

Malware sequence detection

Enhanced neural network architectures that enable the determination and employment of association-based or attention-based 'interrelatedness' of various portions of the input data are provided. A method of employing an architecture includes receiving a first input data element, a second input element, and a third input element. A first interrelated metric that indicates a degree of interrelatedness between the first input data element and the second input data element is determined. A second interrelated metric is determined. The second interrelated metric indicates a degree of interrelatedness between the first input data element and the third input data element. An interrelated vector is generated based on the first interrelated metric and the second interrelated metric. The neural network is employed to generate an output vector that corresponds to the first input vector and is based on a combination of the first input vector and the interrelated vector.

Growing community of inventors

Redmond, WA, United States of America

Rakshit Agrawal