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    Cybersecurity in Robotic Autonomous Vehicles: Machine Learning Applications to Detect Cyber Attacks

    Cybersecurity in Robotic Autonomous Vehicles by Alruwaili, Ahmed; Islam, Sardar M. N.; Gondal, Iqbal;

    Machine Learning Applications to Detect Cyber Attacks

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      • Publisher's listprice GBP 52.99
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        26 818 Ft (25 541 Ft + 5% VAT)
      • Discount 10% (cc. 2 682 Ft off)
      • Discounted price 24 136 Ft (22 987 Ft + 5% VAT)

    26 818 Ft

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    Availability

    Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
    Not in stock at Prospero.

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Short description:

    Cybersecurity in Robotic Autonomous Vehicles introduces a novel Intrusion Detection System (IDS) specifically designed for AVs, which leverages data prioritization in CAN IDs to enhance threat detection and mitigation. It offers a pioneering intrusion detection model for AVs that uses machine and deep learning algorithms.

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    Long description:

    Cybersecurity in Robotic Autonomous Vehicles introduces a novel intrusion detection system (IDS) specifically designed for AVs, which leverages data prioritisation in CAN IDs to enhance threat detection and mitigation. It offers a pioneering intrusion detection model for AVs that uses machine and deep learning algorithms.


    Presenting a new method for improving vehicle security, the book demonstrates how the IDS has incorporated machine learning and deep learning frameworks to analyse CAN bus traffic and identify the presence of any malicious activities in real time with high level of accuracy. It provides a comprehensive examination of the cybersecurity risks faced by AVs with a particular emphasis on CAN vulnerabilities and the innovative use of data prioritisation within CAN IDs.


    The book will interest researchers and advanced undergraduate students taking courses in cybersecurity, automotive engineering, and data science. Automotive industry and robotics professionals focusing on Internet of Vehicles and cybersecurity will also benefit from the contents.

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    Table of Contents:

    1. Introduction.  2. Theoretical Lens.  3. Exploring CAN Bus Security: Insights and Analysis.  4. Research Design.  5. Results and Discussion.  6. Conclusions and Future Research.  

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