Samsung Electronics Co., Ltd.

Fujitsu Corporation

Wei Yang

Mukul Ranjan Prasad

Hongxia Jin

Peng Ning

Ahmed M Azab

Xun Chen

Ruowen Wang

Haining Chen

Khaled Elwazeer

Deguang Kong

David Thomson

A method of providing continuous user authentication for resource access control includes launching a continuous authentication service at a boot time of a first device, wherein the first device includes a processor, a memory, and one or more sensors configured to collect authentication information. Additionally, the method includes receiving authentication information comprising one or more of explicit authentication information or implicit authentication information, and receiving a request for access to a resource of the first device. Further, the method includes the operations of determining, by the continuous authentication service, a current value of a security state, the current value of the security state based in part on a time interval between a receipt time of the authentication information and a current time and controlling access to the resource based on the current value of the security state.

System and method for resource access authentication

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 method of modeling behavior of a mobile application includes determining an action associated with the mobile application based on source code of the mobile application. The method further includes dynamically crawling through the mobile application based on the action and generating a graphical user interface (GUI) behavioral model based on dynamically crawling through the mobile application.

Automatically extracting a model for the behavior of a mobile application

According to an aspect of an embodiment, a method may include determining a set of entity instances in a software program. Each entity instance may correspond to a program entity through which the software program performs an interaction with an external entity that is external to the software program. The method may also include determining an identity of each external entity. Additionally, the method may include determining a set of data-flow predicates and a set of control-flow predicates that are satisfied by the software program. Further, the method may include comparing the set of data-flow predicates and the set of control-flow predicates with a signature library that includes one or more malicious software signatures. The method may further include determining that the software program is malicious in response to the set of data-flow predicates and the set of control-flow predicates matching one or more malicious software signatures.

Detection of malicious software behavior using signature-based static analysis

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Sunnyvale, CA, United States of America

Wei Yang