Deep Learning-based Intelligent Algorithms for Effective Transmission Authentication and Anomaly Identification in Vehicular Networks

Vehicular networks enable effective traffic control and security on the roads. Sustaining security and network performance depends on authentic transmission and anomaly detection. The inherent delays in conventional approaches to transmission authentication and anomaly detection limit their utility in actual-time vehicular network conditions. In this paper, we present the Deep Attentive Optimized Bidirectional Long/Short Term Memory (DABLSTM) method. The method combines the advantages of deep learning with those of attention mechanisms and bidirectional LSTM. The dataset was gathered from the Hacking and Countermeasure Research Lab (HCRL). Min-max normalization is used for data pre-processing, and then Principal Component Analysis (PCA) is used for efficient feature extraction, which optimizes the source data for our method. The DA-BLSTM method examines the approach's efficiency by evaluating its performance achieves the accuracy (91.2%), recall rate (92.8%), precision (92.3%), and F1-score (93.4%). The DA-BLSTM prevails over traditional techniques by displaying lower latency and considerably higher accuracy in detecting proper transmissions and anomalies within vehicular network data. We present a promising approach for real-time, safe, reliable, and effective authentication and anomaly identification in vehicle networks, establishing an innovative standard for vehicular communication safety.