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

Andrew Jones; Jeremy Straub

doi:10.1117/12.2264072

1 May 2017 Using deep learning to detect network intrusions and malware in autonomous robots

Andrew Jones, Jeremy Straub

Proceedings Volume 10185, Cyber Sensing 2017; 1018505 (2017) https://doi.org/10.1117/12.2264072
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

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.

Citation Download Citation

Andrew Jones and Jeremy Straub "Using deep learning to detect network intrusions and malware in autonomous robots", Proc. SPIE 10185, Cyber Sensing 2017, 1018505 (1 May 2017); https://doi.org/10.1117/12.2264072

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