Robust malware detection using deep eigenspace learning

Abstract

A system, method and computer-readable medium for detecting and diffusing malware. Malware is analyzed to generate signatures and determine a fixing moment. There has always been a problem in differentiating between the attack vector and the payload. So if the attack vector in the Web pages with malicious content, chat rooms, malicious e-mail attachments, etc. then the payload can be treated as the viruses and executable. By using deep eigenspace learning approach, to identify functional codes to a vector space and to categorize malicious web sites and malicious Applications. So to prove the strength of the proposed approach to its stability against malware detection and trash Code insertion attacks. Finally, A Junk code injection attack is a malware anti-forensic technique against functional code inspection. As the name suggests, junk code insertion may include the addition of functional code sequences, which do not run in malware or inclusion of instructions that do not make any difference in malware activities.