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

College of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
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Appl. Sci. 2018, 8(5), 676; https://doi.org/10.3390/app8050676
Received: 31 March 2018 / Revised: 19 April 2018 / Accepted: 24 April 2018 / Published: 26 April 2018
(This article belongs to the Special Issue Security and Privacy for Cyber Physical Systems)
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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. View Full-Text
Keywords: security; heavy-duty robots; IDBN; dynamic model; detection; mitigation security; heavy-duty robots; IDBN; dynamic model; detection; mitigation
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Li, L.; Xie, L.; Li, W.; Liu, Z.; Wang, Z. Improved Deep Belief Networks (IDBN) Dynamic Model-Based Detection and Mitigation for Targeted Attacks on Heavy-Duty Robots. Appl. Sci. 2018, 8, 676.

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