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A Comprehensive Analysis of Datasets for automotive Intrusion Detection Systems

Computers, Materials & Continua 2023年第9期76卷 3413-3442页

作者： Seyoung Lee Wonsuk Choi InsupKim Ganggyu Lee Dong Hoon Lee School of Cybersecurity Korea UniversitySeoul02841Korea Memory Division Samsung ElectronicsHwaseong18449Korea

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 *** the availability of several datasets for automotive IDSs,there has been a lack of comprehensive analysis focusing on assessing these *** paper aims to address the need for dataset assessment in the context of automotive *** proposes qualitative and quantitative metrics that are independent of specific automotive IDSs,to evaluate the quality of *** metrics take into consideration various aspects such as dataset description,collection environment,and attack *** paper evaluates eight commonly used datasets for automotive IDSs using the proposed *** evaluation reveals biases in the datasets,particularly in terms of limited contexts and lack of ***,it highlights that the attacks in the datasets were mostly injected without considering normal behaviors,which poses challenges for training and evaluating machine learning-based *** paper emphasizes the importance of addressing the identified limitations in existing datasets to improve the performance and adaptability of automotive *** proposed metrics can serve as valuable guidelines for researchers and practitioners in selecting and constructing high-quality datasets for automotive security ***,this paper presents the requirements for high-quality datasets,including the need for representativeness,diversity,and balance.

关键词： Controller area network(CAN) intrusion detection system(IDS) automotive security machine learning(ML) dataset

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FIDS:Filtering-Based Intrusion Detection System for In-Vehicle CAN

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Intelligent Automation & Soft Computing 2023年第9期37卷 2941-2954页

作者： Seungmin Lee Hyunghoon Kim Haehyun Cho Hyo Jin Jo Department of Software Soongsil UniversitySeoul06978Korea

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 ***,since CAN lacks any security technologies,it is vulnerable to cyber *** address this,researchers have conducted studies on machine learning-based intrusion detection systems(IDSs)for ***,most existing IDSs still have non-negligible detection *** this paper,we pro-pose a new filtering-based intrusion detection system(FIDS)to minimize the detection errors of machine learning-based *** uses a whitelist and a blacklist created from CAN *** whitelist stores the cryptographic hash value of normal packet sequences to correct false positives(FP),while the blacklist corrects false negatives(FN)based on transmission intervals and identifiers of CAN *** evaluated the performance of the proposed FIDS by implementing a machine learning-based IDS and applying FIDS to *** conducted the evaluation using two CAN attack datasets provided by the Hacking and Countermeasure Research Lab(HCRL),which confirmed that FIDS can effectively reduce the FP and FN of the existing IDS.

关键词： Controller area network machine learning intrusion detection system automotive security

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Threat Modeling and Risk Assessment in automotive Domain Based on Stochastic Petri Net

Threat Modeling and Risk Assessment in Automotive Domain Bas...

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2020中国汽车工程学会年会暨展览会

作者： Luo Feng Pan Wenwen Hou Shuo Zhang Xuan Yang Zhenyu School of automotive Studies Tongji University

To accommodate modern applications,vehicles have become increasingly *** we get benefit from it,the problem of Cybersecurity becomes more and more ***,we have more and more security and privacy challenges for the design of automotive applications and *** known that threat analysis and risk assessment is the key point in the security life cycle,in this paper,we propose a method of threat modeling and risk assessment in automotive ***,we find the vulnerability and threat of automotive system from existing ***,combining stochastic Petri net with Markov chain,we come up a method for probabilistic risk *** the method,we can analyse and evaluate the system by four ***,a case study of the firmware update is *** indicates that this method is efficient.

关键词： automotive security stochastic petri net threat modeling risk assessment

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