Classification of Encrypted Traffic With Second-Order Markov Chains and Application Attribute Bigrams

With a profusion of network applications, traffic classification plays a crucial role in network management and policy-based security control. The widely used encryption transmission protocols, such as the secure socket layer/transport layer security (SSL/TLS) protocols, lead to the failure of traditional payload-based classification methods. Existing methods for encrypted traffic classification cannot achieve high discrimination accuracy for applications with similar fingerprints. In this paper, we propose an attribute-aware encrypted traffic classification method based on the second-order Markov Chains. We start by exploring approaches that can further improve the performance of existing methods in terms of discrimination accuracy, and make promising observations that the application attribute bigram, which consists of the certificate packet length and the first application data size in SSL/TLS sessions, contributes to application discrimination. To increase the diversity of application fingerprints, we develop a new method by incorporating the attribute bigrams into the second-order homogeneous Markov chains. Extensive evaluation results show that the proposed method can improve the classification accuracy by 29% on the average compared with the state-of-the-art Markov-based method.