Distributed Cyber Attacks Detection and Recovery Mechanism for Vehicle Platooning

Abstract

This paper is concerned with the distributed attack detection and recovery in a vehicle platooning control system, wherein inter-vehicle information is propagated via a wireless communication network. An active adversary may launch malicious cyber attacks to compromise both sensor measurements and control command data due to the openness of the wireless communication. First, a distributed attack detection algorithm is developed to identify any of those attacks. The core of the algorithm lies in that each designed filter can provide two ellipsoidal sets: a state prediction set and a state estimation set. Whether a filter can detect the occurrence of such an attack is determined by the existence of intersection between these two sets. Second, two recovery mechanisms are put forward, through which the adversarial effects of cyber attacks can be mitigated in a timely manner. The recovery mechanisms depend on reliable modifications of the attacked signals required for the computation of the two ellipsoidal sets. Finally, simulation is provided to validate the effectiveness of the proposed method in both detection and recovery phases.

An Ellipsoidal Set-Membership Approach to Distributed Joint State and Sensor Fault Estimation of Autonomous Ground Vehicles

“…This completes the proof. Remark 4: In light of Theorem 1, the proposed distributed joint state and sensor fault estimation problem can be cast into the feasibility problem of a set of recursive linear matrix inequality (RLMI) in (22). Through Theorem 1, one can recursively solve out the estimator i's gain matrix sequence L i for each T-S fuzzy model rule and determine optimized state estimation ellipsoids X i k+1 enclosing the true augmented state of the vehicle despite the existence of UBB external disturbance, UBB measurement noise, incomplete measurement, and sensor saturation.…”

“…Remark 5: The interior-point algorithm usually has a polynomial-time complexity O(RM 3 ), where R is the total row size of the main LMIs, M is the total number of scalar decision variables of the main LMI (22). Since R and M are dependent on n x , n u , n y , n w , n v , and n f , the computational complexity of the developed recursive algorithm depends polynomially on the dimensions of each sensing node's parameter variables.…”

Cyber–Physical Security of Powertrain Systems in Modern Electric Vehicles: Vulnerabilities, Challenges, and Future Visions

“…To improve the cyberphysical security of EVs with four motor drives, [151] proposed a coordinated detection methodology that combines state observer and performance-based evaluation metrics. Currently, the research of cybersecurity of EVs is still at an early stage, and most of the literature focus on driving-level control systems, such as detection and recovery mechanism design for vehicle platooning [19], [20]. Cyber-physical security issues of vehicle powertrain and power electronic systems are not well addressed in both academia and the industries, and few studies have been devoted to this area.…”

“…Further, [3], [12]- [15] presented mitigation techniques and solution frameworks to defend modern vehicles against cyber-attacks such as secure onboard diagnostics (OBD-II) port, better firewall, reliable hardware, secure software updates, penetration testing, and code reviews. In addition, some analytics and detection methodologies for in-vehicle network security [16]- [18] and control systems [19], [20] have been studied. Fig.…”

“…In [22], the stability of the vehicle platoon under jamming attacks was investigated. To defend the vehicles against cyber-attacks, [20] and [23] proposed detection and mitigation strategies to reduce collisions for a vehicle platooning system.…”