Unveiling Sybil Attacks Using <scp>AI</scp>‐Driven Techniques in Software‐Defined Vehicular Networks

ABSTRACT The centralized nature of software‐defined networks (SDN) makes them a suitable choice for vehicular networks. This enables numerous vehicles to communicate within an SD‐vehicular network (SDVN) through vehicle‐to‐vehicle (V2V) and with road‐side units (RSUs) via vehicle‐to‐infrastructure (V2I) connections. The increased traffic volume necessitates robust security solutions, particularly for Sybil attacks. Here, the attacker aims to undermine network trust by gaining unauthorized access or manipulating network communication. While traditional cryptography‐based security methods are effective, their encryption and decryption processes may cause excess delays in vehicular scenarios. Previous studies have suggested machine learning (ML) like AI‐driven approaches for Sybil attack detection in vehicular networks. However, the primary drawbacks are high detection time and feature engineering of network data. To overcome these issues, we propose a two‐phase detection framework, in which the first phase utilizes cosine similarity and weighting factors to identify attack misbehavior in vehicles. These metrics contribute to the calculation of effective node trust (ENT), which helps in further attack detection. In the second phase, deep learning (DL) models such as CNN and LSTM are employed for further granular classification of misbehaving vehicles into normal, fault, or Sybil attack vehicles. Due to the time series nature of vehicle data, CNN and LSTM are used. The methodology deployed at the controller provides a comprehensive analysis, offering a single‐ to multi‐stage classification scheme. The classifier identifies six distinct vehicle types associated with such attacks. The proposed schemes demonstrate superior accuracy with an average of 94.49% to 99.94%, surpassing the performance of existing methods.