Intrusion Detection in Robotic Swarms

Sargeant, Ian; Tomlinson, Allan

doi:10.1007/978-3-030-29513-4_71

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…The coverage area is represented as a convex polygon with an area of 300 m 2 to 1200 m 2 . The number of entities in a robot swarm n r ∈ [10,30]∩N and polygon vertices n v ∈ [3, 7] ∩ N are sampled uniformly. At the start of each coverage task, the robot swarm is randomly positioned within the coverage area.…”

Section: Simulation Datamentioning

confidence: 99%

“…In [15] and [16], the detection of anomalies is investigated in network traffic and physical properties such as power consumption, speed, or residual energy. The research in [10] assumes prior knowledge of signatures of abnormal swarm behavior which can then be used to check against the observed swarm behavior. Further, by examining the effect of a randomized secret control signal on the robot's behavior, [17] proposes to verify that a robot is not controlled by an intruder mimicking normal swarm behavior.…”

Section: Introductionmentioning

confidence: 99%

“…Other antagonistic influences, like the manipulation of communication, are not subject of this study. Indeed, identifying the presence and identity of deliberately antagonistic agents in a robot swarm has received little attention [10], and the authors are not aware of any work that studies antagonistic behavior in the deployment-problem setting. Several papers investigate a robot swarm's ability to reach consensus in a collective decision problem despite the presence of misbehaving agents [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Anomalously acting agents: the deployment problem

Wenger,

Ebel,

Eberhard

2023

Preprint

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The detection of intentionally antagonistic behavior in robot swarms brings about challenges that exceed identifying merely erroneous behavior. We investigate a data-based approach to recognize antagonistic behavior in robots executing a deployment task. The task requires a robot swarm of variable size and start positions to optimally distribute within an arbitrary convex surveillance area. Combining a long short-term memory neural network and a normalizing flow, our approach learns to approximate the probability of a robot action. Thus, actions with low probability density values can be categorized as anomalous. The applicability of the proposed approach is validated on simulated runs containing benevolent, antagonistic, and erroneous robots. Both antagonistic and erroneous robots are detected with an accuracy of more than 90 percent.

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Section: Simulation Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Anomalously acting agents: the deployment problem

Wenger,

Ebel,

Eberhard

2023

Preprint

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“…2. Research based on traffic and behavior analysis; involving the development of technologies and methods aimed at detecting and tracking anomalous values and methods for verifying individual robots [9,10].…”

Section: Introductionmentioning

confidence: 99%

A method of counteracting Byzantine robots with a random behavior strategy during collective design-making in swarm robotic systems

2021

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The active introduction of robotics swarm systems into life brings the issues of their information security up to date. Known security approaches often do not take into account the peculiarities of the implementation of swarm systems, such as collective design-making, and only consider the presence of Byzantine robots with a strategy of behavior consisting in voting against a majority when a consensus is reached. The aim of this work is to increase the security of the collective design-making process in a swarm robotics system from the imposition of false and ineffective alternatives by Byzantine robots with a random behavior strategy. It is proposed to use an approach based on the use of a distributed ledger and analysis of deviations in the process of collective design-making, which will allow identifying and isolating harmful effects. The solution to the problem of detecting Byzantine robots is based on the application of the criterion of the degree of confidence of a robot in choosing an alternative when a consensus is reached by the swarm system and is based on the assumption that the distribution of the degree of confidence of a Byzantine robot due to ignoring the parameters of the external environment and voting for random alternatives is significantly different from the behavior an ordinary robot. The elements of novelty of the presented solution include the use of the degree of confidence criterion to ensure the safety of collective design-making and the ability to take into account various strategies of behavior of Byzantine robots. The use of the presented solution makes it possible to increase the efficiency of reaching consensus by a swarm robotics system in the presence of Byzantine robots. The simulation for a swarm of 20 robots, including 5 Byzantine ones with random behavior, showed an increase in the probability of correctly reaching a consensus by 12.5%. The practical significance of the presented solutions lies in the possibility of ensuring the stability of reaching consensus by a swarm robotics system in the presence of robots with harmful behavior.

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Anomalously acting agents: the deployment problem

Wenger,

Ebel,

Eberhard

2024

Multibody Syst Dyn

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Detecting intentionally antagonistic behavior in robot swarms brings challenges that exceed identifying merely erroneous behavior. We investigate a data-based approach to recognize anomalous and, in particular, antagonistic behavior in robots executing a deployment task. The task requires a robot swarm of variable size and starting positions to be optimally distributed within an arbitrary convex surveillance area. Combining a long short-term memory neural network and a normalizing flow, our approach learns to approximate the probability of a robot action. Thus, actions with low probability density values can be categorized as anomalous. The applicability of the proposed approach is validated on simulated runs containing benevolent, antagonistic, and erroneous robots. Both antagonistic and erroneous robots are detected with more than 90 percent accuracy.

show abstract

Intrusion Detection in Robotic Swarms

Cited by 3 publications

References 23 publications

Anomalously acting agents: the deployment problem

Anomalously acting agents: the deployment problem

A method of counteracting Byzantine robots with a random behavior strategy during collective design-making in swarm robotic systems

Anomalously acting agents: the deployment problem

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