Transforming Transportation: Safe and Secure Vehicular Communication and Anomaly Detection with Intelligent Cyber-Physical System and Deep Learning

Intelligent Cyber-Physical Transportation Systems (ICTS) rely deeply on data exchange between moving vehicles and fixed infrastructure. While exchanging data between vehicles in the network, security is a main constraint. ICTS encompasses many facilities and claims, such as managing road traffic, traveler data, public transportation systems, and autonomous vehicles. It is anticipated that ICTS will make significant contributions to future smart cities and urban design in road and traffic security, transportation and transport optimization, energy efficiency, and environmental pollution control. Flexibility, variety of quality-of-service needs, and massive volumes of data generated all present challenges when working with ITS. Previous works on cyber-physical attack detection lag in ensuring security at various levels. To monitor In-Vehicle Networks (IVN), Vehicle-to-Vehicle (V2V) Communications, and Vehicle-to-Infrastructure (V2I) Networks for malicious behavior, we introduce a deep learning-based Intrusion Detection System (IDS) for ICTS. In this research, we look into how Deep Learning (DL), a relatively new but rapidly growing subject, could help improve ICTS. This research investigates whether deep learning can enhance the security of autonomous vehicles and other forms of smart transportation. We improve the smart transport network using the deep learning method, simulate it, and statistically study its data transmission economy, accuracy projections of the future, and path-switching strategy. To identify malicious activity in the core networks of autonomous vehicles (AV), we have developed an ensemble Long-Short Term Memory (LSTM) technique based on Deep Learning architecture. The car hacking dataset (used to monitor communications within a vehicle) and the UNSWNB15 dataset (used to monitor communications with the outside world) are used to test and compare the performance of the proposed IDS. The experimental findings showed that our suggested system outperformed other intrusion detection methods by achieving 100% accuracy in detecting all sorts of assaults on the auto-hacking dataset and 99% on the UNSW-NB15 dataset.