On mitigating sampling-induced accuracy loss in traffic anomaly detection systems

摘要: Real-time Anomaly Detection Systems (ADSs) use packet sampling to realize traffic analysis at wire speeds. While recent studies have shown that a considerable loss of anomaly detection accuracy is incurred due to sampling, solutions to mitigate this loss are largely unexplored. In this paper, we propose a Progressive Security-Aware Packet Sampling (PSAS) algorithm which enables a real-time inline anomaly detector to achieve higher accuracy by sampling larger volumes of malicious traffic than random sampling, while adhering to a given sampling budget. High malicious sampling rates are achieved by deploying inline ADSs progressively on a packet's path. Each ADS encodes a binary score (malicious or benign) of a sampled packet into the packet before forwarding it to the next hop node. The next hop node then samples packets marked as malicious with a higher probability. We analytically prove that under …