TY - GEN
T1 - Safeguarding Autonomous Transportation
T2 - 10th International Congress on Information and Communication Technology, ICICT 2025
AU - Eziama, Elvin
AU - Diovu, Remigius Chidiebere
AU - Onwujekwe, Gerald
AU - Kapita, Jacob
AU - Jegede, Victor L.Y.
AU - Jegede, Jegede T.T.
AU - Olumba, Solomon G.
AU - Edokpolor, Harrison
AU - Olaniyan, Adeleye
AU - Orenuga, Paul A.
AU - Ikekwere, Anthony C.
AU - Ikekwere, Emmanuel A.
AU - Okonkwo, Uchechukwu
AU - Egwuatu, Egwuatu C.A.
AU - Anyim, Charles
AU - Alebiosu, Jacob A.
AU - Mbogu, Victor N.
AU - Enobakhare, Benjamin O.
AU - Oladimeji, Toheeb A.
AU - Odigie, Anthony Junior
AU - Olufemi, Adeleye
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - By improving reliable communication between cellular vehicle-to-everything (C-V2X), 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%.
AB - By improving reliable communication between cellular vehicle-to-everything (C-V2X), 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%.
KW - Anomaly detection
KW - Connected and automated vehicles (CAVs)
KW - Intelligent transportation systems (ITS)
KW - Vehicle-to-everything (V2X)
UR - https://www.scopus.com/pages/publications/105020008549
U2 - 10.1007/978-981-96-6429-0_47
DO - 10.1007/978-981-96-6429-0_47
M3 - Conference contribution
AN - SCOPUS:105020008549
SN - 9789819664283
T3 - Lecture Notes in Networks and Systems
SP - 611
EP - 623
BT - Proceedings of 10th International Congress on Information and Communication Technology - ICICT 2025
A2 - Yang, Xin-She
A2 - Sherratt, Simon
A2 - Dey, Nilanjan
A2 - Joshi, Amit
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 February 2025 through 21 February 2025
ER -