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In this paper, we propose HyperVision, a realtime unsupervised machinelearning (ML) based malicious traffic detection system. Particularly,HyperVision is able to detect unknown patterns of encrypted malicious trafficby utilizing a compact inmemory graph built upon the traffic patterns. Thegraph captures flow interaction patterns represented by the graph structuralfeatures, instead of the features of specific known attacks. We develop anunsupervised graph learning method to detect abnormal interaction patterns byanalyzing the connectivity, sparsity, and statistical features of the graph,which allows HyperVision to detect various encrypted attack traffic withoutrequiring any labeled datasets of known attacks. Moreover, we establish aninformation theory model to demonstrate that the information preserved by thegraph approaches the ideal theoretical bound. We show the performance ofHyperVision by real-world experiments with 92 datasets including 48 attackswith encrypted malicious traffic. The experimental results illustrate thatHyperVision achieves at least 0.92 AUC and 0.86 F1, which significantlyoutperform the state-of-the-art methods. In particular, more than 50% attacksin our experiments can evade all these methods. Moreover, HyperVision achievesat least 80.6 Gb/s detection throughput with the average detection latency of0.83s.

Detecting Unknown Encrypted Malicious Traffic in Real Time via Flow Interaction Graph Analysis