IIDS: Intelligent Intrusion Detection System for Sustainable Development in Autonomous Vehicles

Connected and Autonomous Vehicles (CAVs) enable various capabilities and functionalities like automated driving assistance, navigation and path planning, cruise control, independent decision making, and low-carbon transportation in the real-time environment. However, the increased CAVs usage renders the potential vulnerabilities in the Internet of Vehicles (IoV) environment, making it susceptible to cyberattacks. An Intrusion Detection System (IDS) is a technique to report network assaults by potential Autonomous Vehicles (AVs) without encryption and authorization procedures for internal and external vehicular communications. This paper proposes an Intelligent IDS (IIDS) to enhance intrusion detection and categorize malicious AVs using a modified Convolutional Neural Network (CNN) with hyperparameter optimization approaches for IoV systems. The proposed IIDS framework works in a 5G Vehicle-to-Everything (V2X) environment to effectively broadcast messages about malicious AVs. Thus IIDS aids in preventing collisions and chaos, enhancing safety monitoring in the traffic. The experimental results depict that the proposed IIDS achieves 98% accuracy in detecting attacks.

Automated summary

Connected and Autonomous Vehicles (CAVs) enable various capabilities and functionalities in the real-time environment, but they also render potential vulnerabilities in the Internet of Vehicles (IoV) environment, making it susceptible to cyberattacks. This paper proposes an Intelligent IDS (IIDS) that uses a modified Convolutional Neural Network (CNN) with hyperparameter optimization approaches to enhance intrusion detection and categorize malicious AVs in IoV systems. The experimental results show that the proposed IIDS achieves 98% accuracy in detecting attacks.