SCRUTINIZER: Detecting Code Reuse in Malware via Decompilation and Machine Learning

Growing numbers of advanced malware-based attacks against governments and corporations, for political, financial and scientific gains, have taken security breaches to the next level. In response to such attacks, both academia and industry have investigated techniques to model and reconstruct these attacks and to defend against them. While such efforts have been all useful in mitigating the effects of modern attacks, automated malware code reuse inspection and campaign attribution have received less attention.