Spoof Resilient Coordination in Distributed and Robust Robotic Networks

Renganathan, Venkatraman; Fathian, Kaveh; Safaoui, Sleiman; Summers, Tyler H.

doi:10.1109/tcst.2021.3063924

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…The same author in [11], [12], and [13] developed a cyber-physical system to prevent Cyberattacks in vehicular and robotic systems as the initial thrust of this research. Renganathan et al in [14] proposed a class of resilient consensus strategies, namely, weighted mean-subsequencereduced (W-MSR) consensus by incorporating a physical layer authentication strategy. Despite their promising results, their algorithms are not suitable to monitor large-scale ROS network traffic data.…”

Section: A Related Workmentioning

confidence: 99%

“…As can be seen, the performance of the system with the independent windowing (IW) technique outperforms the performance of the overlapping windowing technique (OW) (see Figs. [14][15][16][17]. Intuitively, the independent windowing technique can provide more information in each sampling time since it contains no repetitive information with the neighboring windows (segments).…”

Section: Statistical Performancementioning

confidence: 99%

“…14, one can see the true negative rate as defined by T N R = +F N of the systems, suggesting the percentage of legitimate data being correctly identified. Likewise, the figures for independent windows are better thanThis article has been accepted for publication in IEEE Transactions on Dependable and Secure Computing.…”

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confidence: 99%

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Trusted Operations of a Military Ground Robot in the Face of Man-in-the-Middle Cyberattacks Using Deep Learning Convolutional Neural Networks: Real-Time Experimental Outcomes

Santoso,

Finn

2024

IEEE Trans. Dependable and Secure Comput.

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Safe and secure operations of robotic systems are of paramount importance. Aiming for achieving the trusted operation of a military robotic vehicle under contested environments, we introduce a new cyber-physical system based on the concepts of deep learning convolutional neural networks (CNNs). The proposed algorithm is specifically designed to reduce the cyber vulnerability of the Robot Operating System (ROS), a well-known middleware platform widely used in both civilian and military robots. To demonstrate the efficacy of the proposed algorithm, we conduct penetration testing (real-time man-in-the-middle cyberattacks) on the GVR-BOT Unmanned Ground Vehicle (UGV), a military ground robot, developed by the United States Army Combat Capabilities Development Command (CCDC), Ground Vehicle Systems Center. The cyberattacks also exploit the vulnerability of the Robot Operating System (ROS) employed in its onboard computer. We collect experimental data and train our CNN based on two different operating conditions, namely, legitimate and malicious. We normalize and convert the network traffic data in the form of RGB or grayscale images. We introduce two different types of windowing techniques, namely, the independent and overlapping sliding epochs to efficiently feed the network traffic data to our CNN system. Our research indicates the efficacy of the proposed algorithm as our proposed cyber intrusion detection system can achieve reasonably high accuracies ≥ 99 % and substantially small false-positive rates ≤ 2 % supported with minimum detection times. In addition, we also compare and demonstrate the relative merits of our proposed algorithm with respect to the performance of some well-known techniques, namely, 'bag-of-features' and Support Vector Machine (SVM) algorithms.

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Section: A Related Workmentioning

confidence: 99%

Section: Statistical Performancementioning

confidence: 99%

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Trusted Operations of a Military Ground Robot in the Face of Man-in-the-Middle Cyberattacks Using Deep Learning Convolutional Neural Networks: Real-Time Experimental Outcomes

Santoso,

Finn

2024

IEEE Trans. Dependable and Secure Comput.

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“…We make the following assumption that at the beginning of the transmission, the estimator can receive the packet x0 successfully, that is, δ 0 = 1. Based on (10) and (11), it is easy to obtain the estimation error covariance at the remote estimator as P(k) = h τ k ( P).…”

Section: Remote Estimationmentioning

confidence: 99%

“…In terms of specific attack types, cyber attacks encountered on CPSs are mainly divided into DoS attacks [7,8], spoofing attacks [9,10] and injection attacks [11][12][13]. Among them, DoS attacks have been paid more attention as one of the most frequent and easy cyber attacks to implement.…”

Section: Introductionmentioning

confidence: 99%

Security State Estimation for Cyber-Physical Systems against DoS Attacks via Reinforcement Learning and Game Theory

Jin

Zhang

et al. 2022

Actuators

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This paper addressed the optimal policy selection problem of attacker and sensor in cyber-physical systems (CPSs) under denial of service (DoS) attacks. Since the sensor and the attacker have opposite goals, a two-player zero-sum game is introduced to describe the game between the sensor and the attacker, and the Nash equilibrium strategies are studied to obtain the optimal actions. In order to effectively evaluate and quantify the gains, a reinforcement learning algorithm is proposed to dynamically adjust the corresponding strategies. Furthermore, security state estimation is introduced to evaluate the impact of offensive and defensive strategies on CPSs. In the algorithm, the ε-greedy policy is improved to make optimal choices based on sufficient learning, achieving a balance of exploration and exploitation. It is worth noting that the channel reliability factor is considered in order to study CPSs with multiple reasons for packet loss. The reinforcement learning algorithm is designed in two scenarios: reliable channel (that is, the reason for packet loss is only DoS attacks) and unreliable channel (the reason for packet loss is not entirely from DoS attacks). The simulation results of the two scenarios show that the proposed reinforcement learning algorithm can quickly converge to the Nash equilibrium policies of both sides, proving the availability and effectiveness of the algorithm.

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On the non-resiliency of subsequence reduced resilient consensus in multiagent networks

Khalyavin,

Abbas

2024

European Journal of Control

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Spoof Resilient Coordination in Distributed and Robust Robotic Networks

Cited by 6 publications

References 15 publications

Trusted Operations of a Military Ground Robot in the Face of Man-in-the-Middle Cyberattacks Using Deep Learning Convolutional Neural Networks: Real-Time Experimental Outcomes

Trusted Operations of a Military Ground Robot in the Face of Man-in-the-Middle Cyberattacks Using Deep Learning Convolutional Neural Networks: Real-Time Experimental Outcomes

Security State Estimation for Cyber-Physical Systems against DoS Attacks via Reinforcement Learning and Game Theory

On the non-resiliency of subsequence reduced resilient consensus in multiagent networks

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