HawkEye: Cross-Platform Malware Detection with Representation Learning on Graphs

Cross-Platform Malware Contamination

Malware Detection Through Call Graphs

Each day, anti-virus companies receive large quantities of potentially harmful executables. Many of the malicious samples among these executables are variations of earlier encountered malware, created by their authors to evade pattern-based detection. Consequently, robust detection approaches are required, capable of recognizing similar samples automatically. In this thesis, malware detection t...

A Survey on Various Malware Detection Techniques on Mobile Platform

With the rapid arrival of mobile platforms on the market, android Platform has become a market leader in 2015 Q2, according to IDC. As Android has ruling most of the market, the problem of malware threats and security is also increasing. In this review paper, a fastidious study of the terms related to mobile malware and the techniques used for the detection of malware is done. Some proposed met...

Dmia: a Malware Detection System on Ios Platform

iOS is a popular operating system on Apple’s smartphones, and recent security events have shown the possibility of stealing the users' privacy in iOS without being detected, such as XcodeGhost. So, we present the design and implementation of a malware vetting system, called DMIA. DMIA first collects runtime information of an app and then distinguish between malicious and normal apps by a novel ...

Evading Machine Learning Malware Detection

Machine learning is a popular approach to signatureless malware detection because it can generalize to never-beforeseen malware families and polymorphic strains. This has resulted in its practical use for either primary detection engines or supplementary heuristic detections by anti-malware vendors. Recent work in adversarial machine learning has shown that models are susceptible to gradient-ba...