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

【Author】 Anbalagan, Sudha; Raja, Gunasekaran; Gurumoorthy, Sugeerthi; Suresh, R. Deepak; Dev, Kapal

【Source】IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

【影响因子】9.551

【Abstract】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.

【Keywords】Intrusion detection; Security; Real-time systems; Autonomous vehicles; Vehicle-to-everything; Safety; Electronic mail; Deep learning; autonomous vehicles; intrusion detection; safety monitoring; IoV; 5G-V2X

【发表时间】2023 2023 JUN 5

【收录时间】2023-07-08

【文献类型】

【主题类别】

--