Distributed Attack Detection and Secure Estimation of Networked Cyber-Physical Systems Against False Data Injection Attacks and Jamming Attacks

Abstract

This paper is concerned with the problem of joint distributed attack detection and distributed secure estimation for a networked cyber-physical system under physical and cyber attacks. The system is monitored by a wireless sensor network in which a group of sensors is spatially distributed and the sensors' measurements are broadcast to remote estimators via a wireless network medium. A malicious adversary simultaneously launches a false data injection attack at the physical system layer to intentionally modify the system's state and jamming attacks at the cyber layer to block the wireless transmission channels between sensors and remote estimators. The sensors' measurements can be randomly dropped with mathematical probability if the corresponding transmission channels are deliberately jammed by the adversary. Resilient attack detection estimators are delicately constructed to provide locally reliable state estimations and detect the false data injection attack. Then, criteria for analyzing the estimation performance and designing the desired estimators are derived to guarantee the solvability of the problem. Finally, the effectiveness of the proposed approach is shown through an illustrative example.

Cooperative attack strategy design via H−/H∞ scheme for linear cyber‐physical systems

“…The relationship between the attack signal e k and the biases k , k+1 has been discussed in detail, and the impact of the attack signal e k on the Kalman filter of system, the controller and the 2 attack detector has been modeled as the system model (11). Next, based on the model (11), the coordinated attack strategy will be formulated.…”

“…in which k is the states of the system (11), and r k is the external signal designed by the attacker and belongs to L 2 [0, ∞). K 1 andK 2 are the gains to be designed.…”

“…The existing research on how to defend against attacks mainly focuses on security estimation and control, attack detection, 4 and recognition of anticipated attack patterns. For instance, denial-of-service (DoS) attack, [5][6][7][8] false data injection attack, [9][10][11][12] and replay attack. 13,14 In addition, the resilient filtering problem under DoS attack was investigated by Chen et al 15 for the power system.…”

Minimal Sufficient Conditions for Structural Observability/Controllability of Composite Networks via Kronecker Product

“…C ONTROLLABILITY and observability of networked systems arise in situations where a group of interconnected devices are influenced or observed by an external entity; examples range from classical applications in robotic systems, multi-agent networks, and sensor networks, to more recent emerging areas that include social networks, Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) [2]- [9]. In all of these applications, there is a layered network that connects individuals and objects leading to a significant interest in composite networks [10]- [14].…”

Smart Jamming Attacks in Wireless Networks During a Transmission Cycle: Stackelberg Game with Hierarchical Learning Solution

“…Compared to the approaches used in previous works [12], [13], [14], [15], [17], etc. we are interested here in the impact of a smart jammer on the transmitter power levels during the period that starts at the first attempt of a packet transmission until the next packet transmission first attempt, due to the fact that when re-transmissions are used, the jammers cause the effective network activity factor (and hence the interference among the Receiver Sides (RSs) to be doubled [24].…”