Multi-Attack Identification and Mitigation mechanism based on multi-agent collaboration in Vehicular Named Data Networking

This paper introduces a novel Multi-Attack Identification and Mitigation mechanism (MAIM) designed to enhance security within Vehicular Name Data Networking (VNDN), a derivative of Name Data Networking (NDN) optimized for the Internet of Vehicles (IoV). VNDN, while offering improved communication security for mobile networks, is vulnerable to interest flooding attacks. MAIM addresses this issue through a collaborative multi-agent system comprising detection algorithms, an identification model, and a mitigation model. The MAIM mechanism begins with vehicle nodes monitoring traffic and identifying potential threats, relaying this information to Road Side Units (RSUs), which utilize Random Forests to detect attacks. Detected threats are then communicated to the Base Station (BS), which employs Convolutional Neural Networks and Support Vector Machines to analyze and classify the attack type. The RSUs, informed by the BS, use Graph Convolution Networks to isolate malicious nodes, effectively mitigating the attack. Comparative simulation and real-world experiments demonstrate MAIM's superior performance in attack recognition and mitigation, the average accuracy for attack detection is 97.5%, the average accuracy for attack identification reaches 85.2%, while the average interest satisfaction rate under attack suppression stands at 81%, highlighting its potential as a robust solution for securing VNDN environments.