Dataset for Cyber-Physical Anomaly Detection in Smart Homes

Smart homes contain programmable electronic devices (mostly IoT) that enable home au- tomation. People who live in smart homes benefit from interconnected devices by controlling them either remotely or manually/autonomously. However, high interconnectivity comes with an increased attack surface, making the smart home an attractive target for adversaries. NCC Group and the Global Cyber Alliance recorded over 12,000 attacks to log into smart home devices maliciously. Recent statistics show that over 200 million smart homes can be subjected to these attacks. Conventional security systems are either focused on network traffic (e.g., firewalls) or physical environment (e.g., CCTV or basic motion sensors), but not both. A key challenge in de- veloping cyber-physical security systems is the lack of datasets and test beds. For cyber-physical datasets to be meaningful, they need to be collected in real smart home environments. Due to the inherited difficulties and challenges (e.g. effort, costs, test-bed availability), such cyber-physical smart home datasets are quite rare. This paper aims to fill this gap by contributing a dataset we collected in a real smart home with annotated labels. This paper explains the process we followed to collect the data and how we organised them to facilitate wider use within research communities.

A related article can be found at https://doi.org/10.3389/friot.2023.1275080