Web services have been greatly threatened by remote exploit code attacks, where maliciously crafted HTTP requests are used to inject binary code to compromise web servers and web applications. In practice, besides detection of such attacks, attack attribution analysis (i.e., to automatically categorize exploits or determine whether an exploit is a variant of an attack from the past) is also very important. In this paper, we present SA 3 , a novel exploit code attribution analysis that combines semantics-based analysis and statistical modeling to automatically categorize given exploit code. SA 3 extracts semantic features from exploit code through data anomaly analysis and then attributes the exploit to an appropriate class on the basis of our statistical model derived from a Markov model. We evaluate SA 3 over a comprehensive set of shellcode collected from Metasploit and other polymorphic engines. Experimental results show that SA 3 is effective and efficient. The attribution analysis accuracy can be over 90% in different parameter settings with false positive rate no more than 4.5%. The novelty of SA 3 is that it combines semantic analysis with statistical modeling for exploit code attribution analysis.
['Deguang Kong', 'Donghai Tian', 'Qiha Pan', 'Peng Liu', 'Dinghao Wu']
Semantic aware attribution analysis of remote exploits