Classification of Malware Using Structured Control Flow
Malware is a pervasive problem in distributed computer and network systems. Identification of malware variants provides great benefit in early detection. Control flow has been proposed as a characteristic that can be identified across variants, resulting in flow-graph based malware classification. Static analysis is widely used for the classification but can be ineffective if malware undergoes a code packing transformation to hide its real content. This paper proposes a novel algorithm for constructing a control flow graph signature using the decompilation technique of structuring. Similarity between structured graphs can be quickly determined using string edit distances.