An Efficient Cybersecurity Spoofing Attacks DetectionFramework in Vehicular Networks Using Hybridized Dilated and Attention-Based Network

Due to the growing cybersecurity threats, the privacy and security of modern vehicles have become increasingly significant. ‎Cyber offenses in vehicular networks are recognized using an Intrusion Detection System (IDS). The rapid growth of multimedia technologies has made cybersecurity an essential concern.. Automated and internet-connected cars are exposed to spoofing ‎and jammer attacks. GPS location spoofing is an imminent danger to Connected and Autonomous Vehicles (CAV), threatening ‎security and even exposing motorists and pedestrians to risk. Because of flawed protocol design and increased interconnectivity ‎among modern autonomous vehicles, the Controller Area Network (CAN) bus is insecure. The identification of spoofing ‎attacks on the CAN bus is critical. Hence, it is necessary to develop an efficient spoofing attack detection method to address the ‎limitations of existing models. The major phases of the developed framework are (a) Data Collection, (b) Data Pre-processing, ‎‎(c) Weighted Feature Selection, and (d) Attack Detection. At first, essential data utilized for the validation is collected in the ‎standard dataset. Further, the gathered time series data is given as input to the data pre-processing phase. Later, the attained pre-‎processed data is utilized to collect the essential features. Further, in the weighted feature selection phase, the Restricted Boltz-‎mann Machine (RBM) technique is utilized to attain the significant features. Unlike the conventional CMPA, the proposed FE-CMPA introduces a fitness-entrenchment mechanism that improves the optimization of RBM feature weights and enhances ‎relief score maximization. Subsequently, the acquired weighted RBM features are provided for the attack detection phase. Fur-‎Furthermore, the spoofing attacks are detected using the developed Hybrid Dilated and Attention-based Network (HD-ANet), ‎which holds the Deep Temporal Convolution Network (DTCN) and Residual Long Short Term Memory (RLSTM) network ‎for effective validation. Hence, the implemented spoofing attack detection model is more secure and achieves a comparatively ‎higher detection rate than traditional approaches in various experimental evaluations.