Detecting cyber attacks in vehicle networks using improved LSTM based optimization methodology

The growing adoption of intelligent transportation systems and connected vehicle networks has raised significant cybersecurity concerns due to their vulnerability to cyberattacks such as spoofing, message tampering, and denial-of-service. Traditional intrusion detection systems struggle to cope with the dynamic and high-volume nature of vehicular data, often leading to high false positives and limited adaptability. To address this problem, this study proposes an enhanced deep learning-based optimization framework for detecting cyberattacks in vehicle networks. The methodology employs the UNSW-NB15 dataset, with data preprocessed using Maximum-Minimum Normalization. Feature extraction is performed using the Discrete Fourier Transform (DFT), capturing frequency-domain patterns indicative of anomalies. Detection is executed through an Improved Long Short-Term Memory (ILSTM) model, whose parameters are optimized using the Crocodile Optimization Algorithm (COA), aiming to maximize classification accuracy. Experimental results demonstrate that the proposed ILSTM-COA model significantly outperforms existing techniques, achieving 98.9% accuracy and showing notable improvements across sensitivity, specificity, and other performance metrics. This model offers a robust, scalable, and real-time solution for safeguarding vehicular networks against evolving cyber threats.