Masquerade attack detection through observation planning for multi-robot systems

The increasing adoption of autonomous mobile robots comes with a rising concern over the security of these systems. In this work, we examine the dangers that an adversary could pose in a multi-agent robot system. We show that conventional multi-agent plans are vulnerable to strong attackers masquerading as a properly functioning agent. We propose a novel technique to incorporate attack detection into the multi-agent path-finding problem through the simultaneous synthesis of observation plans. We show that by specially crafting the multi-agent plan, the induced inter-agent observations can provide introspective monitoring guarantees; we achieve guarantees that any adversarial agent that plans to break the system-wide security specification must necessarily violate the induced observation plan.Accepted manuscrip

Resilience of multi-robot systems to physical masquerade attacks

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 security-critical 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-based formulation of multi-agent pathfinding that yields multi-agent plans that are provably resilient to physical masquerade attacks. This formalization leverages inter-agent observations to facilitate introspective monitoring to guarantee resilience.Accepted manuscrip

Decision tree-based detection of denial of service and command injection attacks on robotic vehicles

Mobile cyber-physical systems, such as automobiles, drones and robotic vehicles, are gradually becoming attractive targets for cyber attacks. This is a challenge because intrusion detection systems built for conventional computer systems tend to be unsuitable. They can be too demanding for resource-restricted cyber-physical systems or too inaccurate due to the lack of real- world data on actual attack behaviours. Here, we focus on the security of a small remote-controlled robotic vehicle. Having observed that certain types of cyber attacks against it exhibit physical impact, we have developed an intrusion detection system that takes into account not only cyber input features, such as network traffic and disk data, but also physical input features, such as speed, physical jittering and power consumption. As the system is resource-restricted, we have opted for a decision tree-based approach for generating simple detection rules, which we evaluate against denial of service and command injection attacks. We observe that the addition of physical input features can markedly reduce the false positive rate and increase the overall accuracy of the detection

Distributed motion misbehavior detection in teams of heterogeneous aerial robots

This paper addresses the problem of detecting possible misbehavior in a group of autonomous mobile robots, which coexist in a shared environment and interact with each other and coordinate according to a set of common interaction rules. Such rules specify what actions each robot is allowed to perform in order to interact with the other members of the group. The rules are distributed, i.e., they can be evaluated only starting from the knowledge of the individual robot and the information the robot gathers from neighboring robots. We consider misbehaving those robots which, because of either spontaneous failures or malicious tampering, do not follow the rules and whose behavior thus deviates from the nominal assigned one. The main contribution of the paper is to provide a methodology to detect such misbehavior by observing the congruence of actual behavior with the assigned rules as applied to the actual state of the system. The presented methodology is based on a consensus protocol on the events observed by robots. The methodology is fully distributed in the sense that it can be performed by individual robots based only on the local available information, it has been theoretically proven and validated with experiments involving real aerial heterogeneous robots

Distributed intrusion detection for secure cooperative multi–agent systems

In this thesis we propose a solution for the problem of detecting intruders in an open set of cooperative agents. An agent can perform a finite set of maneuvers and is modeled by a hybrid system whose state is a continuous and a discrete part, representing the agents' physical evolution and logical variables, respectively. Each agent plans its behavior and chooses the appropriate maneuver to perform following a common set of shared rules designed to ensure the safety of the entire system. Since the number of agents is unknown, and since these agents have a limited knowledge of their neighborhood, they can make decisions based only on their own position, and on the configuration of a limited number of surrounding agents. Such a planning strategy is said to be decentralized. The expounded solution is an Intrusion Detecting System (IDS), based on a decentralized monitoring strategy, performed by several common local monitor modules running on--board each agent. This module tries to evaluate the behavior of neighboring agents by estimating the occurrence of the logical events described in the shared rule set. Since each monitor has a limited vision of its neighbors, in many cases it can remain uncertain about the correctness of the monitored agent's behavior. In order to solve this problem we developed a distributed consensus algorithm which, by introducing communication between agents, enhances the intrusion detection capabilities of single monitors. The effectiveness of our solution has been proved by in-depth simulations and a theoretical demonstration of the convergence of the consensus algorithm

Collision Free Navigation of a Multi-Robot Team for Intruder Interception

In this report, we propose a decentralised motion control algorithm for the mobile robots to intercept an intruder entering (k-intercepting) or escaping (e-intercepting) a protected region. In continuation, we propose a decentralized navigation strategy (dynamic-intercepting) for a multi-robot team known as predators to intercept the intruders or in the other words, preys, from escaping a siege ring which is created by the predators. A necessary and sufficient condition for the existence of a solution of this problem is obtained. Furthermore, we propose an intelligent game-based decision-making algorithm (IGD) for a fleet of mobile robots to maximize the probability of detection in a bounded region. We prove that the proposed decentralised cooperative and non-cooperative game-based decision-making algorithm enables each robot to make the best decision to choose the shortest path with minimum local information. Then we propose a leader-follower based collision-free navigation control method for a fleet of mobile robots to traverse an unknown cluttered environment where is occupied by multiple obstacles to trap a target. We prove that each individual team member is able to traverse safely in the region, which is cluttered by many obstacles with any shapes to trap the target while using the sensors in some indefinite switching points and not continuously, which leads to saving energy consumption and increasing the battery life of the robots consequently. And finally, we propose a novel navigation strategy for a unicycle mobile robot in a cluttered area with moving obstacles based on virtual field force algorithm. The mathematical proof of the navigation laws and the computer simulations are provided to confirm the validity, robustness, and reliability of the proposed methods