On False Data Injection Attack against Building Automation Systems

KNX is one of the most popular protocols for a building automation system (BAS). However, its lack of security makes it subject to a variety of attacks. In this paper, we perform the first study of false data injection attack against a KNX based BAS. We design a man-in-the-middle (MITM) attack to change the data from a temperature sensor and inject false data to the BAS. We model the BAS system and formally analyze the impact of the false data injection attack on the system in term of energy cost. We find a small amount of erroneous input can incur significant energy cost, but is very hard to detect based on sensor data such as temperature alone. Since the MITM attack may disturb the KNX traffic pattern, we design a machine learning (ML) based detection strategy to detect the false data injection attack based on sophisticated features of the KNX telegram inter-arrival time. We perform real-world experiments and validate the presented false data injection attacks and ML detection strategy. We also simulate a BAS system and show that our proposed attack strategies can have a huge impact on BAS power consumption.