Samsung Electronics Co., Ltd.

Deguang Kong

Hongxia Jin

Wei Yang

One embodiment provides a method comprising, in a training phase, receiving one or more malware samples, extracting multi-aspect features of malicious behaviors triggered by the malware samples, determining evolution patterns of the malware samples based on the multi-aspect features, and predicting mutations of the malware samples based on the evolution patterns. Another embodiment provides a method comprising, in a testing phase, receiving a new mobile application, extracting a first set of multi-aspect features for the new mobile application using a learned feature model, and determining whether the new mobile application is a mutation of a malicious application using a learned classification model and the first set of multi-aspect features.

Malware detection by exploiting malware re-composition variations using feature evolutions and confusions

A computing system includes: a control circuit configured to: determine a privacy baseline for controlling communication for a user, determine an application-specific privacy setting for controlling communication for a first executable program associated with the user, generate a user-specific privacy profile based on the privacy baseline and the application-specific privacy setting, the user-specific privacy profile for controlling an application set including a second executable program; and a storage circuit, coupled to the control circuit, configured to store the user-specific privacy profile.

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San Jose, CA, United States of America

Deguang Kong