A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles

• URL: http://arxiv.org/abs/2201.11812v1
• Date: Thu, 27 Jan 2022 21:24:09 GMT
• Title: A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles
• Authors: Li Yang and Abdallah Shami
• Abstract summary: In this paper, a transfer learning and ensemble learning-based IDS is proposed for Internet of Vehicles (IoV) systems. The proposed IDS has demonstrated over 99.25% detection rates and F1-scores on two public benchmark IoV security datasets. This shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks.
• Score: 10.350337750192997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-threats. Due to the lack of authentication and encryption procedures in vehicular networks, Intrusion Detection Systems (IDSs) are essential approaches to protect modern vehicle systems from network attacks. In this paper, a transfer learning and ensemble learning-based IDS is proposed for IoV systems using convolutional neural networks (CNNs) and hyper-parameter optimization techniques. In the experiments, the proposed IDS has demonstrated over 99.25% detection rates and F1-scores on two well-known public benchmark IoV security datasets: the Car-Hacking dataset and the CICIDS2017 dataset. This shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks.

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