Detection of Cyber-attacks to indoor real time localization systems for autonomous robots

Guerrero‐Higueras, Ángel Manuel; DeCastro-García, Noemí; Olivera, Vicente Matellán

doi:10.1016/j.robot.2017.10.006

Cited by 72 publications

(18 citation statements)

References 10 publications

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“…The algorithm relies on machine learning and rule tracking methods. Papers [30], [31] present algorithms which are also based on machine learning. This work, however, uses them to secure the Real-Time Locating System (RTLS).…”

Section: Mobile Robot Cyber-security Surveymentioning

confidence: 99%

Cyber-security for Mobile Service Robots – Challenges for Cyber-physical System Safety

Dudek

Szynkiewicz

2019

JTIT

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A review of the known and an indication of the new threats for cyber-physical robotic systems, caused by cybernetic attacks, serves, in this paper, as a basis for the analysis of the known methods relied upon to detect and mitigate consequences of such attacks. A particular emphasis is placed on threats speciﬁc for cyber-physical systems, as they are a feature distinguishing these systems from their traditional Information and Communication Technologies (ICT) counterparts. Based on the review of literature and own analyses, unresolved issues regarding the cyber-security of robot systems are presented and discussed.

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Section: Mobile Robot Cyber-security Surveymentioning

confidence: 99%

Cyber-security for Mobile Service Robots – Challenges for Cyber-physical System Safety

Dudek

Szynkiewicz

2019

JTIT

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“…Guerrero et al [ 24 ] focused on cyber-security attacks that target Real Time Location Systems (RTLSs), which are critical components for many robots and autonomous systems. The authors showed that such attacks can be detected by machine and supervised learning techniques.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Cyber-Physical Threats on Robotic Platforms

Yousef

AlMajali

Ghalyon

et al. 2018

Sensors

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Robots are increasingly involved in our daily lives. Fundamental to robots are the communication link (or stream) and the applications that connect the robots to their clients or users. Such communication link and applications are usually supported through client/server network connection. This networking system is amenable of being attacked and vulnerable to the security threats. Ensuring security and privacy for robotic platforms is thus critical, as failures and attacks could have devastating consequences. In this paper, we examine several cyber-physical security threats that are unique to the robotic platforms; specifically the communication link and the applications. Threats target integrity, availability and confidential security requirements of the robotic platforms, which use MobileEyes/arnlServer client/server applications. A robot attack tool (RAT) was developed to perform specific security attacks. An impact-oriented approach was adopted to analyze the assessment results of the attacks. Tests and experiments of attacks were conducted in simulation environment and physically on the robot. The simulation environment was based on MobileSim; a software tool for simulating, debugging and experimenting on MobileRobots/ActivMedia platforms and their environments. The robot platform PeopleBotTM was used for physical experiments. The analysis and testing results show that certain attacks were successful at breaching the robot security. Integrity attacks modified commands and manipulated the robot behavior. Availability attacks were able to cause Denial-of-Service (DoS) and the robot was not responsive to MobileEyes commands. Integrity and availability attacks caused sensitive information on the robot to be hijacked. To mitigate security threats, we provide possible mitigation techniques and suggestions to raise awareness of threats on the robotic platforms, especially when the robots are involved in critical missions or applications.

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“…And, more advanced detection methods have been obtained by combining or integrating evolutionary algorithms and neural networks, which have shown better detection performance than general machine learning methods [18]. Guerrero et al [19] studied that systems built using supervised learning could detect real-time positioning system attacks. Furthermore, Goldman et al [20] introduced an automated process of active detection cyber-domain attacks based on theoretical ideas from decision theory and recent research results in neuroscience.…”

Section: Related Workmentioning

confidence: 99%

Improved Deep Belief Networks (IDBN) Dynamic Model-Based Detection and Mitigation for Targeted Attacks on Heavy-Duty Robots

Xie

et al. 2018

Applied Sciences

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Featured Application: The research is mainly used for heavy-duty robots to detect and mitigate target attacks, which come from both the cyber-domain and the physical-domain. Abstract:In recent years, the robots, especially heavy-duty robots, have become the hardest-hit areas for targeted attacks. These attacks come from both the cyber-domain and the physical-domain. In order to improve the security of heavy-duty robots, this paper proposes a detection and mitigation mechanism which based on improved deep belief networks (IDBN) and dynamic model. The detection mechanism consists of two parts: (1) IDBN security checks, which can detect targeted attacks from the cyber-domain; (2) Dynamic model and security detection, used to detect the targeted attacks which can possibly lead to a physical-domain damage. The mitigation mechanism was established on the base of the detection mechanism and could mitigate transient and discontinuous attacks. Moreover, a test platform was established to carry out the performance evaluation test for the proposed mechanism. The results show that, the detection accuracy for the attack of the cyber-domain of IDBN reaches 96.2%, and the detection accuracy for the attack of physical-domain control commands reaches 94%. The performance evaluation test has verified the reliability and high efficiency of the proposed detection and mitigation mechanism for heavy-duty robots.

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Detection of Cyber-attacks to indoor real time localization systems for autonomous robots

Cited by 72 publications

References 10 publications

Cyber-security for Mobile Service Robots – Challenges for Cyber-physical System Safety

Cyber-security for Mobile Service Robots – Challenges for Cyber-physical System Safety

Analyzing Cyber-Physical Threats on Robotic Platforms

Improved Deep Belief Networks (IDBN) Dynamic Model-Based Detection and Mitigation for Targeted Attacks on Heavy-Duty Robots

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