The Future of Automated Malware Generation presented at ekoparty 2012

by Stephan Chenette,

Summary : "Cyber-criminals Have had back-end equivalent to Virus Total Infrastructures to test if malware is Effective against AV scanners for many years, showing attackers are proactively That When building Avoiding malware detection. In this day of age malicious binaries are generated on demand by server-side kits When a victim visits a malicious web page, making reliance Solely based on hash Inadequate solutions. In the last 15 years have evolved detection techniques in an Attempt to keep up with trends attack. In the last few years companies have looked for security solutions supplemental Such as the use of machine learning to detect and mitigate attacks against cyber criminals. Machine Learning (ML), though not a new concept, is all the rage these days, touted as the next big thing in defensive technology. While ML is beginning to be used in the detection of polymorphic malware, let's not pretend attackers Are not Also experimenting with ML to create advanced malware can bypass Which learning algorithms and heuristics. I will present work to show how attackers might be utilizing ML offensively, in a supervised learning mode, to expose common features to avoid or in order to Use Alternatively Increase the chances of bypassing That Use binary AV scanners and heuristics for ML detection."