Secure Navigation of Robots in Adversarial Environments

Bianchin, Gianluca; Liu, Yinchen; Pasqualetti, Fabio

doi:10.1109/lcsys.2019.2921753

Cited by 19 publications

(11 citation statements)

References 10 publications

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“…First, at the Evaluate phase, robot 5 is selected as r v by the evaluation criteria (41), which has only one in-neighbor and three out-neighbors, and its corresponding 1 hop convex pattern V p 5 exists. Then, during the Cut phase, r a utilizes the learned ĝ and makes consequent excitations on r v by solving (42), such that r v is gradually pulled out of the interaction range of its in-neighbors, shown as the phase before the break time in Fig. 8(a).…”

Section: B Results and Analysismentioning

confidence: 99%

“…For instance, a noise-generating attack was proposed in [40] to alter gyroscopic sensor data, leading to drone crashes. [41], [42] proposed a spoofing attack to disturb the GPS sensor readings, gradually causing trajectory deviations. These attacks are against a specific transducer by utilizing its sensing mechanism, and are hard to be generalized to other scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…Iteratively, r a updates the control inputs until V p i is formed, then the control mode is turned to (42).…”

Section: B Sneak Strategy: Evaluate-cut-restorementioning

confidence: 99%

See 2 more Smart Citations

Sneak Attack against Mobile Robotic Networks under Formation Control

Li¹,

He²,

Ding³

et al. 2021

Preprint

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The security of mobile robotic networks (MRNs) has been an active research topic in recent years. This paper demonstrates that the observable interaction process of MRNs under formation control will present increasingly severe threats. Specifically, we find that an external attack robot, who has only partial observation over MRNs while not knowing the system dynamics or access, can learn the interaction rules from observations and utilize them to replace a target robot, destroying the cooperation performance of MRNs. We call this novel attack as sneak, which endows the attacker with the intelligence of learning knowledge and is hard to be tackled by traditional defense techniques. The key insight is to separately reveal the internal interaction structure within robots and the external interaction mechanism with the environment, from the coupled state evolution influenced by the model-unknown rules and unobservable part of the MRN. To address this issue, we first provide general interaction process modeling and prove the learnability of the interaction rules. Then, with the learned rules, we design an Evaluate-Cut-Restore (ECR) attack strategy considering the partial interaction structure and geometric pattern. We also establish the sufficient conditions for a successful sneak with maximum control impacts over the MRN. Extensive simulations illustrate the feasibility and effectiveness of the proposed attack.

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Section: B Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Sneak Attack against Mobile Robotic Networks under Formation Control

Li¹,

He²,

Ding³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Algorithm 3 is composed of three parts: i) First, before R v comes near to p e , R a needs to wait for the best attack time [Line 17 -21]; ii) Then, after R a begins its initial attack, every iteration of following attacks are based on sampling to explore the motion space of both R a and R v and select a best attack input from a feasible attack set [Line [6][7][8][9][10][11][12][13][14][15][16]; iii) In the end, when R v is close enough from p trap , R a stops and we call the attack is successful. The bottleneck of Algorithm 3 is that the sampling set can be large such that the total computation is time-consuming [Line 8 -14].…”

Section: B Shortest-path Attackmentioning

confidence: 99%

“…Moreover, there are also a few work focusing on physical attacks, where the physical components are considered as attack target to make the attacks stealthy. For instance, the GPS sensor readings can be disturbed by GPS spoofing attack [11]- [13]. Designed acoustic noises can alter gyroscopic sensor data, leading to drone crashes [14].…”

Section: Introductionmentioning

confidence: 99%

Learning-based Intelligent Attack against Formation Control with Obstacle-avoidance

Chen

et al. 2019

2019 American Control Conference (ACC)

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The security issue of mobile robots have attracted considerable attention in recent years. Most existing works focus on detection and countermeasures for some classic attacks from cyberspace. Nevertheless, those work are generally based on some prior assumptions for the attacker (e.g., the system dynamics is known, or internal access is compromised). A few work are delicated to physical attacks, however, there still lacks certain intelligence and advanced control design. In this paper, we propose a physical-based and intelligent attack framework against the obstacle-avoidance of mobile robots. The novelty of our work lies in the following: i) Without any prior information of the system dynamics, the attacker can learn the detection area and goal position of a mobile robot by trial and observation, and the obstacle-avoidance mechanism is learned by support vector regression (SVR) method; ii) Considering different attack requirements, different attack strategies are proposed to implement the attack efficiently; iii) The framework is suitable for holonomic and non-holonomic mobile robots, and the algorithm performance analysis about time complexity and optimality is provided. Furthermore, the condition is obtained to guarantee the success of the attack. Simulations illustrate the effectiveness of the proposed framework.The authors are with the

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Secure trajectory planning against undetectable spoofing attacks

2020

Self Cite

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This paper studies, for the first time, the trajectory planning problem in adversarial environments, where the objective is to design the trajectory of a robot to reach a desired final state despite the unknown and arbitrary action of an attacker. In particular, we consider a robot moving in a two-dimensional space and equipped with two sensors, namely, a Global Navigation Satellite System (GNSS) sensor and a Radio Signal Strength Indicator (RSSI) sensor. The attacker can arbitrarily spoof the readings of the GNSS sensor and the robot control input so as to maximally deviate its trajectory from the nominal precomputed path. We derive explicit and constructive conditions for the existence of undetectable attacks, through which the attacker deviates the robot trajectory in a stealthy way. Conversely, we characterize the existence of secure trajectories, which guarantee that the robot either moves along the nominal trajectory or that the attack remains detectable. We show that secure trajectories can only exist between a subset of states, and provide a numerical mechanism to compute them. We illustrate our findings through several numerical studies, and discuss that our methods are applicable to different models of robot dynamics, including unicycles. More generally, our results show how control design affects security in systems with nonlinear dynamics.

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Secure Navigation of Robots in Adversarial Environments

Cited by 19 publications

References 10 publications

Sneak Attack against Mobile Robotic Networks under Formation Control

Sneak Attack against Mobile Robotic Networks under Formation Control

Learning-based Intelligent Attack against Formation Control with Obstacle-avoidance

Secure trajectory planning against undetectable spoofing attacks

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