Deepak Venugopal

TL;DR: In this paper, a system and method for detecting malware on a mobile platform in a mobile network is presented, which verifies that an executable is malware free by computing the checksum of the executable and comparing that checksum with a checksum obtained from a malware-free copy of the same executable.

TL;DR: In this paper, a wireless intrusion prevention system and method to prevent, detect, and stop malware attacks is presented, which monitors network communications for events characteristic of a malware attack, correlates a plurality of events to detect a malware attacks, and performs mitigating actions to stop the malware attack.

TL;DR: In this article, a management server is configured to provide malware protection for one or more client mobile platforms in communication with the management server via a mobile network, and the malware scanning agent of the client mobile platform using a device independent secure management protocol based at least in part on malware detected in the mobile network.

TL;DR: In this article, a system and method for detecting malware by modeling the behavior of malware and comparing a suspect executable with the model is presented, where feature elements from malware-infected applications, groups the feature elements into feature sets, and develops rules describing a malicious probability relationship between feature elements.

TL;DR: This study proposes a novel hierarchical supervised-learning approach to increase the likelihood of detecting anomalies by analyzing several user features and then characterizing their collective behavior in a unified manner, which can improve the performance of fake reviewer detection on digital platforms.