Evaluation Framework for Anomaly Detection
Project Overview
Develops guidance, datasets, and tooling to evaluate IoT anomaly detection techniques across heterogeneous smart home testbeds.
Anomaly detection techniques are challenging to evaluate, especially when developed using different testbeds and conditions. Smart built environments generate heterogeneous sensor data including temperature readings, status commands, energy consumption metrics, and encrypted metadata. A core challenge is that testbed development has often been secondary to anomaly detection technique creation, making it difficult to compare results across different systems.
This project addresses this gap through four key objectives: conducting a comprehensive literature review on IoT anomaly detection testbeds, identifying smart home testbed characteristics that affect detection quality, developing techniques for data capture, annotation, and modelling, and generating realistic synthetic datasets to benchmark cyber-physical attack detection while measuring trade-offs against live detection approaches.
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