In-Vehicle Network Attack Detection Across Vehicle Models: A Supervised-Unsupervised Hybrid Approach

Nakamura, Shu; Takeuchi, Koh; Kashima, Hisashi; Kishikawa, Takeshi; Ushio, Takashi; Haga, Tomoyuki; Sasaki, Takuma

doi:10.1109/itsc48978.2021.9564896

Cited by 1 publication

(3 citation statements)

References 13 publications

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“…Experimental results that used the HCRL OTIDS and two real datasets showed that the proposed model outperformed selected baseline models. Nakamura et al [117] proposed a hybrid model of a LightGBM-based supervised model and an autoencoder-based unsupervised model. Time differences of consecutive CAN IDs, CAN ID, and payload values were used as the features.…”

Section: Unsupervisedmentioning

confidence: 99%

“…Novikova et al [124] grouped the data considering the [20,106,155]used the timestamp (as a time interval for consecutive IDs), decimal ID, and payload fields as features. In contrast, limited works [117,133] used the binary ID and payload instead of decimal conversion. This increases the dimensionality of the features.…”

Section: Payload-based Featuresmentioning

confidence: 99%

“…The creation of additional features such as entropy-based features helps detect a wide variety of injection attacks. Zero padding was used by the authors [117,133,155,156] to replace the missing values of the CAN payload. This helps obtain a uniform data field to train AI algorithms.…”

Section: Payload-based Featuresmentioning

confidence: 99%

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AI-Based Intrusion Detection Systems for In-Vehicle Networks: A Survey

Rajapaksha¹,

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Al-Kadri

et al. 2023

ACM Comput. Surv.

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The Controller Area Network (CAN) is the most widely used in-vehicle communication protocol, which still lacks the implementation of suitable security mechanisms such as message authentication and encryption. This makes the CAN bus vulnerable to numerous cyber attacks. Various Intrusion Detection Systems (IDSs) have been developed to detect these attacks. However, the high generalization capabilities of Artificial Intelligence (AI) make AI-based IDS an excellent countermeasure against automotive cyber attacks. This article surveys AI-based in-vehicle IDS over the period of 2016-2022 (August) with a novel taxonomy. It reviews the detection techniques, attack types, features, and benchmark datasets. Furthermore, the paper discusses the security of AI models, necessary steps to develop AI-based IDSs in the CAN bus, identifies the limitations of existing proposals and gives recommendations for future research directions.

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Section: Unsupervisedmentioning

confidence: 99%