Event Details
SeniorSentry: Safeguarding AgeTech Devices and Sensors Using Contextual Anomaly Detection and Supervised Machine Learning
Presenter: Achyuth Nandikotkur
Supervisor:
Date: Mon, August 14, 2023
Time: 15:00:00 - 00:00:00
Place: ZOOM - Please see below.
ABSTRACT
Zoom link: https://uvic.zoom.us/j/84275514310?pwd=M0FSckpZQzZZNURldmRTUnBFUzBjQT09
Abstract: With the ever-growing reliance on IoT-enabled sensors to age in place, a need arises to protect them from malicious attacks and detect malfunctions. In an IoT smart home, it is reasonable to hypothesize that sensors near one another can exhibit linear or nonlinear correlations. If substantiated, this property can be beneficial for constructing relationship trends between them and, consequently, detecting attacks or other anomalies by measuring the deviation of their readings against these trends. 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 experimental setup. 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 critical focal point for security. Typically, these sensors are paired with a micro-controller that has an inbuilt communication module (e.g., Bluetooth/Wi-Fi), 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 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.