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Proceedings Paper

Using deep learning to detect network intrusions and malware in autonomous robots
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Paper Abstract

Cybersecurity threats to autonomous robots present a particular danger, as compromised robots can directly and catastrophically effect their surroundings. A two-staged intrusion detection system is proposed which consists of a signature detection component and an anomaly detection component. The anomaly detection component utilizes a deep neural network that is trained to detect commands that deviate from expected behavior. This paper presents ongoing work on the development and testing of this system and concludes with a discussion of directions for future work.

Paper Details

Date Published: 1 May 2017
PDF: 6 pages
Proc. SPIE 10185, Cyber Sensing 2017, 1018505 (1 May 2017); doi: 10.1117/12.2264072
Show Author Affiliations
Andrew Jones, North Dakota State Univ. (United States)
Jeremy Straub, North Dakota State Univ. (United States)


Published in SPIE Proceedings Vol. 10185:
Cyber Sensing 2017
Igor V. Ternovskiy; Peter Chin, Editor(s)

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