Transductive malware label propagation: Find your lineage from your neighbors

The numerous malware variants existing in the cyberspace have posed severe threats to its security. Supervised learning techniques have been applied to automate the process of classifying malware variants. Supervised learning, however, suffers in situations where we have only scarce labeled malware samples. In this work, we propose a transductive malware classification framework, which propagates label information from labeled instances to unlabeled ones. We improve the existing Harmonic function approach based on the maximum confidence principle. We apply this framework on the structural information collected from malware programs, and propose a PageRank-like algorithm to evaluate the distance between two malware programs. We evaluate the performance of our method against the standard Harmonic function method as well as two popular supervised learning techniques. Experimental results suggest that our method outperforms these existing approaches in classifying malware variants when only a small number of labeled samples are available.