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.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    UAV Fault and Anomaly Detection Using Autoencoders

    Dhakal, Raju / Bosma, Carly / Chaudhary, Prachi et al. | IEEE | 2023


    Using deep autoencoders for in-vehicle audio anomaly detection

    Pereira, Pedro José / Coelho, Gabriel José Dias / Ribeiro, Alexandrine et al. | BASE | 2021

    Freier Zugriff

    CANnolo: An Anomaly Detection System based on LSTM Autoencoders for Controller Area Network

    Longari, Stefano / Valcarcel, Daniel Humberto Nova / Zago, Mattia et al. | BASE | 2021

    Freier Zugriff

    Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio

    Coelho, Gabriel / Matos, Luis Miguel / Pereira, Pedro Jose et al. | BASE | 2022

    Freier Zugriff

    Comparative Study of Autoencoders-Its Types and Application

    Shah, Nidhi / Ganatra, Amit | IEEE | 2022