A Comparative Analysis on the use of Autoencoders for Robot Security Anomaly Detection While robots are more and more deployed among people in public spaces, the impact of cybersecurity 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 cybersecurity 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.

Comparison of Semisupervised Deep Neural Networks for Anomaly Detection in Industrial Processes Anomaly detection methods are used for fast and reliable detection of abnormal events in industrial processes. The early detection of anomalies can avoid critical process breakdowns and hence can increase the overall productivity of the system. The availability of labelled datasets for all the possible faulty scenarios is generally not possible, as most of the industrial systems operate in a nonfaulty condition. Deep learning architectures that can be trained in an unsupervised setting such as deep autoencoders, denoising autoencoder and variational autoencoder provide an appropriate solution to this problem of unlabelled data for industrial anomaly detection. We investigate and compare the applicability of these architectures on the benchmark Tennessee Eastman fault detection study. The deep architectures are trained to model only the normal operating condition with its threshold set by kernel density estimation. A detailed comparison from the experimental results shows superior anomaly detection capabilities of the variational autoencoder as compared to the other methods.