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V2V 통신 환경에서 DoS 공격 탐지 모델 개발을 위한 데이터 생성 및 검증
이현로(Hyeonro Lee),이민종(Minjong Lee),하재철(Jaecheol Ha) 한국산학기술학회 2024 한국산학기술학회논문지 Vol.25 No.1
In recent years, autonomous vehicles have been using deep learning to recognize road conditions and make driving decisions. In addition, autonomous driving that uses only deep learning technology has limitations, so it utilizes vehicular ad-hoc network (VANET) communications. However, VANET communications contains vulnerabilities that can be exposed to cyber-attacks such as denial of service (DoS), and research is underway to defend against them. In this paper, we generate a dataset to develop a machine learning model that can detect DoS attacks in the V2V communications environment of VANETs. The dataset is generated using simulation tools, such as OMNeT++, SUMO, Veins, and INET, to reflect the attributes of V2V communications and characteristics of the attacks. In addition, the attack dataset generated is validated to see if attacks can be detected by various machine learning models. The evaluation results show that the generated dataset can detect DoS attacks with an accuracy of about 97% or higher from most of the trained machine learning models, which is useful for training intrusion detection models.