Buoy Sensor Cyberattack Detection in Offshore Petroleum Cyber-Physical Systems

Frequently occurred oil leaking accidents can induce significant damage to the ocean ecosystem and environment. Integrated buoy sensing, which functions as a tool for periodically monitoring oil existence, plays an essential role in oil leakage detection in an offshore petroleum Internet of Things (IoT) and Cyber Physical System (CPS). Buoy sensor cyberattack can severely affect the ability to detect the petroleum leakage and hence delay the pollution recovery process. Despite these, existing techniques seldom deal with attacks on buoy sensors and their impacts on marine oil spill detection. In this article, a Partially observable Markov decision process based Buoy Sensor Cyberattack detection (PBSC) technique is proposed. Proposed PBSC technique utilizing Partially Observable Markov Decision Process (POMDP) method, which is a stochastic process based on Markov decision process, to evaluate the cyberattack probability for each buoy sensors. Cyberattack probability is evaluated by cross entropy based oil simulation method. This technique can efficiently identify attacked sensors and locate the oil leaking sources, which facilitates future pollution recovery. Experimental results from a marine area in Shenzhen, China demonstrate that the proposed technique can improve the detection accuracy by up to 50 percent while ruining x6 faster than the state of art cyberattack techniques.