Experimental Evaluation of Malware Family Classification Methods from Sequential Information of TLS-Encrypted Traffic

In parallel with the rapid adoption of transport layer security (TLS), malware has utilized the encrypted communication channel provided by TLS to hinder detection from network traffic. To this end, recent research efforts are directed toward malware detection and malware family classification for TLS-encrypted traffic. However, amongst their feature sets, the proposals to utilize the sequential information of each TLS session has not been properly evaluated, especially in the context of malware family classification. In this context, we propose a systematic framework to evaluate the state-of-the-art malware family classification methods for TLS-encrypted traffic in a controlled environment and discuss the advantages and limitations of the methods comprehensively. In particular, our experimental results for the 10 representations and classifier combinations show that the graph-based representation for the sequential information achieves better performance regardless of the evaluated classification algorithms. With our framework and findings, researchers can design better machine learning based classifiers