Due to the fast development of the Internet of Things (IoT) in smart buildings, the efficiency of operations, personal comfort, and sustainable operations have all been enhanced. But with all this dependence on potentially linked systems comes vital cybersecurity weaknesses. When such weaknesses are used to attack, they may lead to the compromise of the security of both an individual device as well as a building infrastructure. The present paper introduces a new cybersecure intelligent sensor framework that will be able to protect smart buildings against a wide variety of IOT related cyberattacks. The key element within this structure is a sophisticated AI automated intrusion detector (IDS) that can recognize, categorize and eliminate prospective threats in an instant using machine learning routines. The system uses the combination of intelligent sensor networks with AI-based analytics to continuously observe the environment and system data, with anomalous behaviours being indicators of a security breach being detected. The combination of predictive modelling and automated threat responses will allow the proposed system to achieve resilience against many attacks, including but not limited to denial of service, unauthorized access, and data manipulation. Widespread simulation and testing have shown that the system has a high detection rate, low false alarms, and a fast response time to help secure infrastructure buildings in smart buildings whilst Downtime is minimal. The results demonstrate the possible future of AI-enhanced cybersecurity systems in developing the IoT-based smart building security and enjoyment.
Siam et al. (Mon,) studied this question.