DÏoT: A Federated Self-learning Anomaly Detection System for IoT

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IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many networks already have vulnerable IoT devices that are easy to compromise. This has led to a new category of malware specifically targeting IoT devices. However, existing intrusion detection techniques are not effective in detecting compromised IoT devices given the massive scale of the problem in terms of the number of different types of devices and manufacturers involved. In this paper, we present DIoT, an autonomous self-learning distributed system for detecting compromised IoT devices. DIoT builds effectively on device-type-specific communication profiles without human intervention nor labeled data that are subsequently used to detect anomalous deviations in devices' communication behavior, potentially caused by malicious adversaries. DIoT utilizes a federated learning approach for aggregating behavior profiles efficiently. To the best of our knowledge, it is the first system to employ a federated learning approach to anomaly-detection-based intrusion detection. Consequently, DIoT can cope with emerging new and unknown attacks. We systematically and extensively evaluated more than 30 off-the-shelf IoT devices over a long term and show that DIoT is highly effective (95.6% detection rate) and fast (257 ms) at detecting devices compromised by, for instance, the infamous Mirai malware. DIoT reported no false alarms when evaluated in a real-world smart home deployment setting.