Visible to the public Detecting Cyber-Physical Threats in an Autonomous Robotic Vehicle Using Bayesian Networks

TitleDetecting Cyber-Physical Threats in an Autonomous Robotic Vehicle Using Bayesian Networks
Publication TypeConference Paper
Year of Publication2017
AuthorsBezemskij, A., Loukas, G., Gan, D., Anthony, R. J.
Conference Name2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
KeywordsBayes methods, Bayesian networks, command injection attacks, composability, cyber-physical domain, Intrusion detection, Metrics, Monitoring, pubcrawl, resilience, Resiliency, Robot sensing systems, Support vector machines

Robotic vehicles and especially autonomous robotic vehicles can be attractive targets for attacks that cross the cyber-physical divide, that is cyber attacks or sensory channel attacks affecting the ability to navigate or complete a mission. Detection of such threats is typically limited to knowledge-based and vehicle-specific methods, which are applicable to only specific known attacks, or methods that require computation power that is prohibitive for resource-constrained vehicles. Here, we present a method based on Bayesian Networks that can not only tell whether an autonomous vehicle is under attack, but also whether the attack has originated from the cyber or the physical domain. We demonstrate the feasibility of the approach on an autonomous robotic vehicle built in accordance with the Generic Vehicle Architecture specification and equipped with a variety of popular communication and sensing technologies. The results of experiments involving command injection, rogue node and magnetic interference attacks show that the approach is promising.

Citation Keybezemskij_detecting_2017