Visible to the public Adaptive Intrusion Detection of Malicious Unmanned Air Vehicles Using Behavior Rule Specifications

TitleAdaptive Intrusion Detection of Malicious Unmanned Air Vehicles Using Behavior Rule Specifications
Publication TypeJournal Article
Year of Publication2014
AuthorsMitchell, R., Ing-Ray Chen
JournalSystems, Man, and Cybernetics: Systems, IEEE Transactions on
Date PublishedMay
Keywordsactuators, adaptive intrusion detection, airborne system, ant colony optimisation, ant-colony clustering model, autonomous aerial vehicles, behavior rule specifications, BRUIDS, distributed system, Gears, IDS, Intrusion detection, malicious attacks, malicious unmanned air vehicles, Monitoring, multi-agent systems, multiagent system, opportunistic attacker behaviors, Pricing, random attacker behaviors, reckless attacker behaviors, security, security of data, Sensors, UAV, unmanned air vehicles (UAVs), Weapons

In this paper, we propose an adaptive specification-based intrusion detection system (IDS) for detecting malicious unmanned air vehicles (UAVs) in an airborne system in which continuity of operation is of the utmost importance. An IDS audits UAVs in a distributed system to determine if the UAVs are functioning normally or are operating under malicious attacks. We investigate the impact of reckless, random, and opportunistic attacker behaviors (modes which many historical cyber attacks have used) on the effectiveness of our behavior rule-based UAV IDS (BRUIDS) which bases its audit on behavior rules to quickly assess the survivability of the UAV facing malicious attacks. Through a comparative analysis with the multiagent system/ant-colony clustering model, we demonstrate a high detection accuracy of BRUIDS for compliant performance. By adjusting the detection strength, BRUIDS can effectively trade higher false positives for lower false negatives to cope with more sophisticated random and opportunistic attackers to support ultrasafe and secure UAV applications.

Citation Key6573382