Distinguishing a Human or Machine Cyberattacker In the world of cybersecurity, attacks may come from many sources. It is a problem faced by all users, because cyberattacks have become ubiquitous. Although many types of attacks are relatively simple to defend given commercially available detection software, the attacks are becoming more sophisticated. In more recent times, it is possible that a cyber attack might be initiated by a human or by an automated software attack or bot. One line of defense that has not been sufficiently explored is the advantage that might come to a defender if he or she has the ability to detect whether or not the attacker is human or machinebased. In this paper, we attempt to develop methods to allow a defender to try to determine the type of human or bot attack.

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.