An IoT-Fuzzy-Based Jamming Detection and Recovery System in Wireless Video Surveillance System

Wireless video surveillance system is one of the cyber-physical security systems kinds, which transmits the signal of IP cameras through a wireless medium using a radio band. WVSSs are widely deployed with large systems for use in strategic places such as city centers, public transportation, public roads, airports, and play a significant role in critical infrastructure protection. WVSSs are vulnerable to jamming attacks creating an unwanted denial of service. Hence, it is essential to secure this system from jamming attacks. In this paper, three models of IoT-fuzzy inference system-based jamming detection system are proposed for detecting and countermeasure the presence of jamming by computing two jamming detection metrics; PDR and PLR, and based on the result, the system countermeasures this attack by storing the video feed locally in the subsystem nodes. FIS models are based on Mamdani, Tsukamoto, and Sugeno fuzzy logic which optimizes the jamming detection metrics for detecting the jamming attack. The efficiency of these proposed models is compared in detecting jamming signals. The experimental results show that the proposed Tsukamoto model detects jamming attacks with high accuracy and efficiency. Finally, the proposed IoT-Tsukamoto-based model was compared with the existing systems and proved to be superior to them in terms of central processing complexity, accuracy, and countermeasure for this attack.