Intelligent Transportation Systems (ITS) combine emerging communication, computer, and system technologies to deliver intelligent road traffic services and optimize decision making within the transportation infrastructure. The advancement of connected vehicles, which generate dynamic data through wireless communications, enables ITS to improve their efficiency, especially in Traffic Signal Control (TSC), which is the backbone of traffic flow scheduling. However, wireless communications channels are vulnerable to various types of cyberattacks and can pose serious threats to dynamic TSC systems. Attackers may attempt to manipulate normal traffic flows and cause severe traffic congestion. Deep Reinforcement Learning (DRL) is a powerful technique that has been used to improve TSC systems performance in real-time environments. However, it can be used by attackers to exploit the dynamics of the ITS and learn the optimal attack policy under the lack of deterministic information about system behavior. In this work, to highlight and exploit existing vulnerabilities in TSC systems, we leverage DRL to create an intelligent Sybil attack on a traffic intersection, wherein connected vehicles with fake identities are optimally placed to alter traffic signal timings by corrupting traffic data. The results show that this attack leads to substantial increase in the vehicles’ travel time and yields disastrous traffic congestion, especially if carried out for a prolonged period of time, which will give rise to serious problems such as higher fuel consumption and air pollution in heavily dense cities. In the presence of such intelligent attacks, the design assumptions of existing TSC systems become highly questionable.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Reinforcement Learning-driven Attack on Road Traffic Signal Controllers


    Beteiligte:


    Erscheinungsdatum :

    26.07.2021


    Format / Umfang :

    2719740 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Deep reinforcement learning traffic signal control poisoning attack method based on Trojan horse attack

    XU DONGWEI / WANG DA / LI CHENGBIN | Europäisches Patentamt | 2022

    Freier Zugriff


    Reinforcement learning-based traffic signal control

    Sheng, Liu Tian / Azman, Afizan Bin / Khan, Navid Ali et al. | IEEE | 2024


    Multi-agent road traffic signal control method based on deep reinforcement learning algorithm

    LIU LIJUAN / SI HUA | Europäisches Patentamt | 2023

    Freier Zugriff