Intrusion Detection on QUIC Traffic: A Machine Learning Approach

Abstract

Since the introduction of QUIC protocol, a major change has affected the Internet transport layer, which improves user experience with some security threats. Developed by Google in 2012, QUIC provides a low latency, connection-oriented and encrypted transport. In addition to the encryption capability of QUIC, it overcomes many issues found in the current transport protocols, such as the high-latency connection establishment in TCP. On the other hand, studies on the security analysis of QUIC's key establishment showed several drawbacks. Moreover, the encryption mechanism of the protocol allows adversarial Command & Control (C2) packets to blind with regular QUIC traffic without raising any alarms. Therefore, in this study, we develop a machine learning approach based on fingerprinting that can be used in intrusion detection systems to detect malicious C2 QUIC traffic. To demonstrate the effectiveness of this approach, we conducted an experiment and tested the performance of six machine learning classifiers. The results show that by utilizing the fingerprint, most of the classifiers recognized malicious C2 traffic with an average accuracy of 98%.