Malware Family Discovery Using Reversible Jump MCMC Sampling of Regimes
提出一种基于可逆跳跃马尔可夫链蒙特卡洛的方法，从恶意软件的动态指令轨迹中推断状态切换模式，并通过状态匹配衡量相似性来识别恶意软件家族，对网络安全分析师有用。
Malware is computer software that has either been designed or modified with malicious intent. Hundreds of thousands of new malware threats appear on the internet each day. This is made possible through reuse of known exploits in computer systems that have not been fully eradicated; existing pieces of malware can be trivially modified and combined to create new malware, which is unknown to anti-virus programs. Finding new software with similarities to known malware is therefore an important goal in cyber-security. A dynamic instruction trace of a piece of software is the sequence of machine language instructions it generates when executed. Statistical analysis of a dynamic instruction trace can help reverse engineers infer the purpose and origin of the software that generated it. Instruction traces have been successfully modeled as simple Markov chains, but empirically there are change points in the structure of the traces, with recurring regimes of transition patterns. Here, reversible jump Markov chain Monte Carlo for change point detection is extended to incorporate regime-switching, allowing regimes to be inferred from malware instruction traces. A similarity measure for malware programs based on regime matching is then used to infer the originating families, leading to compelling performance results.