RF Jamming BERT Intrusion Detection Systems for Vehicular Networks

As vehicular networks become increasingly complex and interconnected, they face rising security threats, particularly Radio-Frequency (RF) jamming attacks that can disrupt communication and compromise safety. While existing Artificial Intelligence (AI) models have been applied for RF jamming detection, they often face challenges in addressing the dynamic nature of vehicular environments. In this paper, we present an intrusion detection system (IDS) utilizing Bidirectional Encoder Representations from Transformers (BERT), a transformer-based Large Language Model (LLM) renowned for its contextual understanding. During the training of BERT, we incorporated and experimented with two tokenizing techniques: WordPiece and Byte-Pair Encoding (BPE). The results from our experiments highlighted that the BERT model fine-tuned with the WordPiece tokenization method significantly outperformed existing baseline methods. Specifically, this approach achieved a high accuracy of 96.25% at 25m/s and 90.25% at 15m/s showcasing strong performance gains compared to baseline methods: K-Nearest Neighbors (KNN), Random Forest (RaFo), Long Short-Term Memory (LSTM) that reached, respectively, 94.46% and 82.27%; 94.61% and 80.04%; 95.17% and 84.83% for 25 m/s and 15 m/s. The BPE tokenization IDS version achieved an accuracy of 85.00% at 25m/s and 80.25% at 15m/s. Our results demonstrate the effectiveness of the BERT RF Jamming IDS with WordPiece tokenization to detect and classify RF threats on vehicle networks.