Unknown malware detection using network traffic classification

We present an end-to-end supervised based system for detecting malware by analyzing network traffic. The proposed method extracts 972 behavioral features across different protocols and network layers, and refers to different observation resolutions (transaction, session, flow and conversation windows). A feature selection method is then used to identify the most meaningful features and to reduce the data dimensionality to a tractable size. Finally, various supervised methods are evaluated to indicate whether traffic in the network is malicious, to attribute it to known malware families and to discover new threats. A comparative experimental study using real network traffic from various environments indicates that the proposed system outperforms existing state-of-the-art rule-based systems, such as Snort and Suricata. In particular, our chronological evaluation shows that many unknown malware incidents could have been detected at least a month before their static rules were introduced to either the Snort or Suricata systems.