|Despite the explosion of IoT deployments at Internet Service Provider (ISP) customer networks, such devices remain vulnerable to cyber-attacks. We present a ML-based anomaly detection system, to be deployed at the Customer Premises Equipment (CPE), that leverages several One-Class Classification algorithms and majority voting to detect anomalous network traffic. We train these models using not only conventional per-flow features but also features extracted from sliding windows of flows. An extensive evaluation, using publicly available datasets shows that our algorithm has a higher detection rate than commonly supervised-learning algorithms, which require the use of labelled datasets. Our evaluation suggests that the detection capabilities of our algorithm are only marginally affected by Packet Acceleration, a technique used by CPEs to improve throughput but that reduces the number of packets (per flow) available to extract features from.
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