10th International Congress on Information and Communication Technology

Authors - Elvin Eziama, Remigius Chidiebere Diovu, Gerald Onwujekwe, Jacob Kapita, Victor L.Y. Jegede, Jegede T.T. Jegede, Solomon G. Olumba, Harrison Edokpolor, Adeleye Olaniyan, Paul A. Orenuga, Anthony C. Ikekwere, Emmanuel A. Ikekwere, Uchechukwu Okonkwo, Egwuatu C.A. Egwuatu, Charles Anyim, Jacob A. Alebiosu, Victor N. Mbogu, Benjamin O. Enobakhare, Toheeb A. Oladimeji, Anthony Junior Odigie, Adeleye Olufemi
Abstract - By improving reliable communication between cellular vehicle-to-everything (CV2X), intelligent transportation systems (ITS) have the potential to revolutionize the real-time transportation sector. However, one element that hinders the seamless deployment of ITS is security issues. Resource limitations, anomaly types, false positives, and sensor interference are among the difficulties. Discrete Wavelet-Based Deep Reinforcement Learning with Double Q Learning (DWT-DDQN), a robust hybrid approach that combines the strengths of both discrete wavelet transform (DWT) and Double Deep Q Network (DDQN), is presented in the paper as an integrated mechanism that addresses the majority of these issues. It can dynamically adapt to the network, enhancing the Connected and Automated Vehicles (CAV) system’s safety and dependability. The dynamic approach is achieved by incorporating both the filtering and detection processes, which give a more robust and reliable performance output. Our numerical results clearly demonstrate the superior performance of DWT-DDQN over the existing conventional method at low and high levels of attack rates of α levels of 1% and 3%, and 5% and 7%.