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Cloud Virtual Private Server (VPS) services provide the chance of rapid deployment of anonymous proxy services, becoming an important part of many anonymous proxy solutions. The anonymous system represented by ShadowSocks (SS), through proxy services deployed on VPSs provided by different cloud service providers, has become an important mean for illegal network activists to engage in illegal network activities such as cyber-attacks and darknet transactions. It is difficult for local network administrators to supervise SS traffic from the cloud. While from the local network, the task faces the challenges of Invisible Negotiation Process and Data Transparent Transmission. In this paper, we present a novel SS detection method based on flow context and host behavior. The method can not only accurately identify SS flows, but also be applicable to a large-scale network environment. In this method, we extract 12-dimensional features from three aspects: the relationship between flows, hosts' flow behavior, and hosts' DNS behavior to build the detection model. Among them, the four features about flow burst and the feature of unassociated domain names' number are innovatively proposed in this paper. Moreover, the big data statistical and association techniques are used in the method. To verify the effectiveness of the method, we first built a real SS running environment based on the campus network and two VPSs on two different public cloud platforms. Moreover, we conduct a series of experiments on the NTCI-BDP data platform which is a big data platform built by our team. The experimental results show that our method achieves 93.43% accuracy on experimental data sets and can effectively identify SS traffic.INDEX TERMS Big Data association, cloud-based anonymous proxy, flow burst, flow context, host behavior, traffic identification, shadowsocks.

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