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
01.01.2019
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Use Of Autoencoders , Cyber Operations , Abnormal Samples , F1 Score , System Logs , Malware , Research In The Field , Robotic System , Normal Samples , Convolutional Neural Network , Social Robots , Classification Model , Lexicographic , Impact Of Attacks , Abnormal Situations , Binary Image , Anomalous Behavior , Image Size , Hidden Markov Model , Training Phase , Autonomous Surface Vehicles , Anomaly Detection , Water Quality , Values Of Variables , Feed-forward Network , Robot Behavior , One-class Classification , Transformation Function , Good Classification Performance , Convolutional Layers
DDC: | 629 |
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