Seyoung Lee¹, Wonsuk Choi¹, Insup Kim², Ganggyu Lee², Dong Hoon Lee^1,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3413-3442, 2023, DOI:10.32604/cmc.2023.039583

Abstract Recently, automotive intrusion detection systems (IDSs) have emerged as promising defense approaches to counter attacks on in-vehicle networks (IVNs). However, the effectiveness of IDSs relies heavily on the quality of the datasets used for training and evaluation. Despite the availability of several datasets for automotive IDSs, there has been a lack of comprehensive analysis focusing on assessing these datasets. This paper aims to address the need for dataset assessment in the context of automotive IDSs. It proposes qualitative and quantitative metrics that are independent of specific automotive IDSs, to evaluate the quality of datasets. These metrics take into consideration various… More >

Seungmin Lee, Hyunghoon Kim, Haehyun Cho, Hyo Jin Jo^*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2941-2954, 2023, DOI:10.32604/iasc.2023.039992

Abstract Modern vehicles are equipped with multiple Electronic Control Units (ECUs) that support various convenient driving functions, such as the Advanced Driver Assistance System (ADAS). To enable communication between these ECUs, the Controller Area Network (CAN) protocol is widely used. However, since CAN lacks any security technologies, it is vulnerable to cyber attacks. To address this, researchers have conducted studies on machine learning-based intrusion detection systems (IDSs) for CAN. However, most existing IDSs still have non-negligible detection errors. In this paper, we propose a new filtering-based intrusion detection system (FIDS) to minimize the detection errors of machine learning-based IDSs. FIDS uses… More >