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Smart city article details
| Title | An Intelligent Dynamic Cyber Physical System Threat Detection System For Ensuring Secured Communication In 6G Autonomous Vehicle Networks |
|---|---|
| ID_Doc | 8506 |
| Authors | Shanthalakshmi M.; Ponmagal R.S. |
| Year | 2024 |
| Published | Scientific Reports, 14, 1 |
| DOI | http://dx.doi.org/10.1038/s41598-024-70835-3 |
| Abstract | Smart cities have developed advanced technology that improves people’s lives. A collaboration of smart cities with autonomous vehicles shows the development towards a more advanced future. Cyber-physical system (CPS) are used blend the cyber and physical world, combined with electronic and mechanical systems, Autonomous vehicles (AVs) provide an ideal model of CPS. The integration of 6G technology with Autonomous Vehicles (AVs) marks a significant advancement in Intelligent Transportation Systems (ITS), offering enhanced self-sufficiency, intelligence, and effectiveness. Autonomous vehicles rely on a complex network of sensors, cameras, and software to operate. A cyber-attack could interfere with these systems, leading to accidents, injuries, or fatalities. Autonomous vehicles are often connected to broader transportation networks and infrastructure. A successful cyber-attack could disrupt not only individual vehicles but also public transportation systems, causing widespread chaos and economic damage. Autonomous vehicles communicate with other vehicles (V2V) and infrastructure (V2I) for safe and efficient operation. If these communication channels are compromised, it could lead to collisions, traffic jams, or other dangerous situations. So we present a novel approach to mitigating these security risks by leveraging pre-trained Convolutional Neural Network (CNN) models for dynamic cyber-attack detection within the cyber-physical systems (CPS) framework of AVs. The proposed Intelligent Intrusion Detection System (IIDS) employs a combination of advanced learning techniques, including Data Fusion, One-Class Support Vector Machine, Random Forest, and k-Nearest Neighbor, to improve detection accuracy. The study demonstrates that the EfficientNet model achieves superior performance with an accuracy of up to 99.97%, highlighting its potential to significantly enhance the security of AV networks. This research contributes to the development of intelligent cyber-security models that align with 6G standards, ultimately supporting the safe and efficient integration of AVs into smart cities. © The Author(s) 2024. |
| Author Keywords | Autonomous Vehicles (AV); Controller area network (CAN); Cyber-physical system (CPS); Intrusion detection system (IDS); Transfer learning (TL) |
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