Abstract:The advent of autonomous mobile multi-robot systems has driven innovation in both the industrial and defense sectors. The integration of such systems in safety-and securitycritical applications has raised concern over their resilience to attack. In this work, we investigate the security problem of a stealthy adversary masquerading as a properly functioning agent. We show that conventional multi-agent pathfinding solutions are vulnerable to these physical masquerade attacks. Furthermore, we provide a constraint… Show more
“…We employ the APMAPF solver ( [2]) to generate a MAPF plan with a co-observation schedule in a grid-world application (Fig. 3a).…”
Section: Secured Planning Results and Limitationmentioning
confidence: 99%
“…We compute the differential of H implicitly using its definition (2). We use the notation [v] × : R 3 → R 3×3 to denote the matrix representation of the cross product with the vector v, i.e.,…”
“…This paper addresses a specific scenario in which robots are compromised by attacker and directed to forbidden regions, which may contain security-sensitive equipment or human workers. Such countering deliberate deviations, termed plandeviation attacks, are identified and addressed in previous studies [2]- [6]. As a security measure, we utilized onboard sensing capabilities of the robots to perform inter-robot coobservations and check for unusual behavior.…”
Section: Introductionmentioning
confidence: 99%
“…A preliminary version of the paper was presented in [5], [6]. Extanding the grid-world solution from [2] to continuous configuration spaces, we incorporate the co-observation planning This project is supported by the National Science Foundation grant "CPS: Medium: Collaborative Research: Multiagent Physical Cognition and Control Synthesis Against Cyber Attacks" (Award number 1932162).…”
This paper addresses security challenges in multirobot systems (MRS) where adversaries may compromise robot control, risking unauthorized access to forbidden areas. We propose a novel multi-robot optimal planning algorithm that integrates mutual observations and introduces reachability constraints for enhanced security. This ensures that, even with adversarial movements, compromised robots cannot breach forbidden regions without missing scheduled co-observations. The reachability constraint uses ellipsoidal over-approximation for efficient intersection checking and gradient computation. To enhance system resilience and tackle feasibility challenges, we also introduce sub-teams. These cohesive units replace individual robot assignments along each route, enabling redundant robots to deviate for co-observations across different trajectories, securing multiple sub-teams without requiring modifications. We formulate the cross-trajectory co-observation plan by solving a network flow coverage problem on the checkpoint graph generated from the original unsecured MRS trajectories, providing the same security guarantees against plan-deviation attacks. We demonstrate the effectiveness and robustness of our proposed algorithm, which significantly strengthens the security of multi-robot systems in the face of adversarial threats.
“…We employ the APMAPF solver ( [2]) to generate a MAPF plan with a co-observation schedule in a grid-world application (Fig. 3a).…”
Section: Secured Planning Results and Limitationmentioning
confidence: 99%
“…We compute the differential of H implicitly using its definition (2). We use the notation [v] × : R 3 → R 3×3 to denote the matrix representation of the cross product with the vector v, i.e.,…”
“…This paper addresses a specific scenario in which robots are compromised by attacker and directed to forbidden regions, which may contain security-sensitive equipment or human workers. Such countering deliberate deviations, termed plandeviation attacks, are identified and addressed in previous studies [2]- [6]. As a security measure, we utilized onboard sensing capabilities of the robots to perform inter-robot coobservations and check for unusual behavior.…”
Section: Introductionmentioning
confidence: 99%
“…A preliminary version of the paper was presented in [5], [6]. Extanding the grid-world solution from [2] to continuous configuration spaces, we incorporate the co-observation planning This project is supported by the National Science Foundation grant "CPS: Medium: Collaborative Research: Multiagent Physical Cognition and Control Synthesis Against Cyber Attacks" (Award number 1932162).…”
This paper addresses security challenges in multirobot systems (MRS) where adversaries may compromise robot control, risking unauthorized access to forbidden areas. We propose a novel multi-robot optimal planning algorithm that integrates mutual observations and introduces reachability constraints for enhanced security. This ensures that, even with adversarial movements, compromised robots cannot breach forbidden regions without missing scheduled co-observations. The reachability constraint uses ellipsoidal over-approximation for efficient intersection checking and gradient computation. To enhance system resilience and tackle feasibility challenges, we also introduce sub-teams. These cohesive units replace individual robot assignments along each route, enabling redundant robots to deviate for co-observations across different trajectories, securing multiple sub-teams without requiring modifications. We formulate the cross-trajectory co-observation plan by solving a network flow coverage problem on the checkpoint graph generated from the original unsecured MRS trajectories, providing the same security guarantees against plan-deviation attacks. We demonstrate the effectiveness and robustness of our proposed algorithm, which significantly strengthens the security of multi-robot systems in the face of adversarial threats.
“…Masquerade. The attacker uses a fake identity and IP spoofing to pretend to be a legitimate network user in order to steal information from the system or network [21,22]. Then, by launching a brute force attack, stolen passwords can be used to gain unauthorized access to important information [23].…”
In today’s Industrial Internet of Things (IIoT) environment, where different systems interact with the physical world, the state proposed by the Industry 4.0 standards can lead to escalating vulnerabilities, especially when these systems receive data streams from multiple intermediaries, requiring multilevel security approaches, in addition to link encryption. At the same time taking into account the heterogeneity of the systems included in the IIoT ecosystem and the non-institutionalized interoperability in terms of hardware and software, serious issues arise as to how to secure these systems. In this framework, given that the protection of industrial equipment is a requirement inextricably linked to technological developments and the use of the IoT, it is important to identify the major vulnerabilities and the associated risks and threats and to suggest the most appropriate countermeasures. In this context, this study provides a description of the attacks against IIoT systems, as well as a thorough analysis of the solutions for these attacks, as they have been proposed in the most recent literature.
Purpose of Review
Robotic patrolling aims at protecting a physical environment by deploying a team of one or more autonomous mobile robots in it. A key problem in this scenario is characterizing and computing effective patrolling strategies that could guarantee some level of protection against different types of threats. This paper provides a survey of contributions that represent the recent research trends to deal with such a challenge.
Recent Findings
Starting from a set of basic and recurring modeling landmarks, the formulations of robotic patrolling studied by current research are diverse and, to some extent, complementary. Some works propose optimal approaches where the objective function is based on the idleness induced by the patrolling strategy on locations of the environment. On-line methods focus on handling events that can dynamically alter the patrolling task. Adversarial methods, where an underlying game-theoretical interaction with an attacker is modeled, consider sophisticated attacker behaviors.
Summary
The wide spectrum of heterogenous approaches and techniques shows a common trend of moving towards more realistic models where constraints, dynamic environments, limited attacker capabilities, and richer strategy representations are introduced. The results provide complementarities and synergies towards more effective robotic patrolling systems, paving the way to a set of interesting open problems.
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