Nowadays traffic on the Internet has been widely encrypted to protect its confidentiality and privacy. However, traffic encryption is always abused by attackers to conceal their malicious behaviors. Since encrypted malicious traffic is similar to benign flows, it can easily evade traditional detection. In particular, the existing encrypted traffic detection methods are supervised which rely on the prior knowledge of known attacks (e.g., labeled datasets). Detecting unknown encrypted malicious traffic, which does not require prior knowledge, is still an open problem. In this paper, we propose HyperVision, an unsupervised machine learning (ML) based malicious traffic detection system. Particularly, HyperVision is able to detect unknown patterns of encrypted malicious traffic by utilizing a graph built upon flow interaction patterns, instead of learning the features of specific known attacks. We develop an unsupervised graph learning method to detect abnormal interaction patterns by analyzing the graph features, which allows HyperVision to detect unknown attacks without requiring any labeled datasets. Moreover, we establish an information theory model to prove the effectiveness of HyperVision. We show the performance of HyperVision by real-world experiments with 140 attacks. The experimental results illustrate that HyperVision outperforms the state-of-the-art methods by 13.9% accuracy improvement. Moreover, HyperVision achieves 15.82 Mpps detection throughput with the average detection latency of 0.29s.