While robots are more and more deployed among people in public spaces, the impact of cyber-security attacks is significantly increasing. Most of consumer and professional robotic systems are affected by multiple vulnerabilities and the research in this field is just started. This paper addresses the problem of automatic detection of anomalous behaviors possibly coming from cyber-security attacks. The proposed solution is based on extracting system logs from a set of internal variables of a robotic system, on transforming such data into images, and on training different Autoencoder architectures to classify robot behaviors to detect anomalies. Experimental results in two different scenarios (autonomous boats and social robots) show effectiveness and general applicability of the proposed method.
A Comparative Analysis on the use of Autoencoders for Robot Security Anomaly Detection
2019-01-01
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Anomaly Detection , Use Of Autoencoders , Research In The Field , Robotic System , Anomalous Behavior , Robot Behavior , Social Robots , Cyber Operations , Impact Of Attacks , System Logs , Values Of Variables , Classification Model , Convolutional Neural Network , Water Quality , Convolutional Layers , Normal Samples , F1 Score , Image Size , Training Phase , Hidden Markov Model , Abnormal Samples , Autonomous Surface Vehicles , One-class Classification , Binary Image , Feed-forward Network , Malware , Lexicographic , Good Classification Performance , Transformation Function , Abnormal Situations
DDC: | 629 |
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