SeniorSentry: Safeguarding AgeTech Devices and Sensors Using Contextual Anomaly Detection and Supervised Machine Learning




Nandikotkur, Achyuth

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With the ever-growing reliance on IoT-enabled sensors to age in place, a need arises to protect them from malicious activities by detecting attacks or other anomalies. In this work, we first confirm the presence of correlations between co-located sensors by statistically analyzing two public smart-home datasets and a dataset we collected from our lab. Then, we leverage the sliding window approach and supervised machine learning to develop a novel contextual-anomaly-detection model that reaches a true positive rate of 89.47% and a false positive rate of 0%. Furthermore, as homes become smarter with these IoT sensors, the underlying communication technology they employ becomes a target for attackers. Typically, these sensors are paired with a micro-controller that has an inbuilt communication module (e.g., Bluetooth/WiFi), to form an edge device that facilitates communication. Monitoring vitals, climate control, illumination control, fall detection, incontinence detection, pill dispensing, and several other functions are successfully addressed by these devices. The family of vulnerabilities recently found in the the Link Manager Protocol (LMP) and baseband layers of the Bluetooth Classic (BT Classic) stack called BrakTooth, poses a genuine threat to the availability of such devices. In response, our research introduces a cost-effective experimental active sniffer that captures traffic at both these layers of the BT Classic stack and utilizes supervised machine learning to detect Braktooth-based attacks.



IoT security, anomaly detection, correlation, mutual information, BrakTooth