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

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.