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<p>The rapid growth of connected vehicles in vehicular ad-hoc networks (VANETs), with frequent data exchanges between vehicles and roadside units (RSUs) enhances traffic efficiency but also increases the attack surface, as adversaries can exploit the high communication density to launch malicious activities. Specifically, attacks such as denial-of-service (DoS), where attackers flood the RSU with excessive packets or fake requests, under dense traffic conditions where each vehicle already transmits periodic safety messages, this artificial load quickly overwhelms the RSU processing capacity, leading to congestion and service disruption. To mitigate such risks, intrusion detection systems (IDSs) are essential, as they monitor network traffic in real time, identify malicious patterns, and prevent attacks before they compromise critical services. Existing deep learning-based IDSs algorithm often treat these threats in isolation, overlooking the semantic relationships among different attack types. To address this gap, we propose a novel knowledge graph-based deep convolutional neural network (KG-DeepCNN) IDS for securing VANETs. The proposed IDS is deployed at the RSU level (RSU-IDS), where incoming vehicle and packet information are analyzed to distinguish legitimate requests from malicious ones, thereby strengthening vehicle-to-vehicle (V2V) communication security. The knowledge graph enriches raw data by capturing semantic correlations among attack patterns, which are then processed by an ensemble of CNN learners with diverse architectures for improved classification accuracy. The framework is evaluated on four benchmark datasets—BoT-IoT, ToN-IoT, UNSW-NB15, and NSL-KDD—achieving accuracies of 99%, 98%, 98%, and 99%, respectively. Notably, the model improves detection accuracy on the UNSW-NB15 dataset by 2.01% compared to state-of-the-art approaches, demonstrating its robustness and scalability for VANET security.</p>

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Mitigating intrusion attacks in VANETs through reliable communication with graph-based deep learning approaches

  • 期刊论文

  • 发表日期：

    2025年10月15日

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  • Ashish Kumari,
  • Shailender Kumar

摘要

The rapid growth of connected vehicles in vehicular ad-hoc networks (VANETs), with frequent data exchanges between vehicles and roadside units (RSUs) enhances traffic efficiency but also increases the attack surface, as adversaries can exploit the high communication density to launch malicious activities. Specifically, attacks such as denial-of-service (DoS), where attackers flood the RSU with excessive packets or fake requests, under dense traffic conditions where each vehicle already transmits periodic safety messages, this artificial load quickly overwhelms the RSU processing capacity, leading to congestion and service disruption. To mitigate such risks, intrusion detection systems (IDSs) are essential, as they monitor network traffic in real time, identify malicious patterns, and prevent attacks before they compromise critical services. Existing deep learning-based IDSs algorithm often treat these threats in isolation, overlooking the semantic relationships among different attack types. To address this gap, we propose a novel knowledge graph-based deep convolutional neural network (KG-DeepCNN) IDS for securing VANETs. The proposed IDS is deployed at the RSU level (RSU-IDS), where incoming vehicle and packet information are analyzed to distinguish legitimate requests from malicious ones, thereby strengthening vehicle-to-vehicle (V2V) communication security. The knowledge graph enriches raw data by capturing semantic correlations among attack patterns, which are then processed by an ensemble of CNN learners with diverse architectures for improved classification accuracy. The framework is evaluated on four benchmark datasets—BoT-IoT, ToN-IoT, UNSW-NB15, and NSL-KDD—achieving accuracies of 99%, 98%, 98%, and 99%, respectively. Notably, the model improves detection accuracy on the UNSW-NB15 dataset by 2.01% compared to state-of-the-art approaches, demonstrating its robustness and scalability for VANET security.