HVAC Attack Detection Using Novel Machine Learning Model

HVAC systems are heating, ventilation, and air conditioning units that allow for maintaining indoor air conditions to a desired degree. Other than maintaining occupant comfort, its use in industrial applications and critical infrastructure is vital to maintain operations since many systems and components must operate under controlled environments, such as, to avoid overheating. HVAC systems by design do not incorporate security. Given this, most HVAC systems , especially those connected to the internet are vulnerable to cyber attacks from various angles. Such an example was the Stuxnet virus which sabotaged a nuclear plant in 2010. Due to this and among other events, HVAC security is now under the spotlight, and the need for a solid security model for HVAC security is necessary. In this paper, we present an anomaly-based detection machine learning model to detect malicious sabotage of HVAC systems which achieves an accuracy of 99% and recall of 98.2%.