VehicleEIDS: A Novel External Intrusion Detection System Based on Vehicle Voltage Signals

Xun, Yijie; Zhao, Yilin; Li, Jiajia

doi:10.1109/jiot.2021.3090397

Cited by 36 publications

(9 citation statements)

References 32 publications

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“…More features can be derived through the different fields of the CAN data frame using feature engineering techniques, and this increases the attack detection capability. Physical characteristics such as voltage signals were used as a feature in the work of Xun et al [178], and this has the capability to identify attack sources that could not identify with other IDSs discussed. Priorities of IDs were not considered in reviewed literature and will be a good feature to explore.…”

Section: Resultsmentioning

confidence: 99%

“…However, this was tested only on a robotic vehicle. Motivated by the works of Cho and Shin [29] and Choi et al [31], Xun et al [178] proposed VehicleEIDS, a novel IDS based on the vehicle voltage signal. This model utilized the unique voltage signals generated by ECUs.…”

Section: Physical Characteristics-based Detectionmentioning

confidence: 99%

“…Usually, ID and payload values are in hexadecimal (hex) format. In addition to features in the CAN data frame, one work [178] used voltage signals (physical characteristics) as a feature. Figure 5 depicts the standard format of publicly available CAN data [51].…”

Section: Feature Selection and Data Pre-processingmentioning

confidence: 99%

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

Rajapaksha¹,

Kalutarage

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: Resultsmentioning

confidence: 99%

Section: Physical Characteristics-based Detectionmentioning

confidence: 99%

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

Rajapaksha¹,

Kalutarage

Al-Kadri

et al. 2023

ACM Comput. Surv.

View full text Add to dashboard Cite

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“…In [11], the authors proposed a specification-based IDS for in-vehicle network intrusion detection by extracting design specifications of CAN messages. The IDS proposed in [12] used unique voltage signals generated by ECUs as features of the deep support vector domain description model. However, both of these models [11], [12] have a low generalization capability as they require specific knowledge of CAN data.…”

Section: Related Workmentioning

confidence: 99%

“…The IDS proposed in [12] used unique voltage signals generated by ECUs as features of the deep support vector domain description model. However, both of these models [11], [12] have a low generalization capability as they require specific knowledge of CAN data. A one-class compound classifier was used in [13] to detect CAN bus attacks.…”

Section: Related Workmentioning

confidence: 99%

Keep the Moving Vehicle Secure: Context-Aware Intrusion Detection System for In-Vehicle CAN Bus Security

Rajapaksha

Kalutarage

Al-Kadri

et al. 2022

2022 14th International Conference on Cyber Conflict: Keep Moving! (CyCon)

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The growth of information technologies has driven the development of the transportation sector, including connected and autonomous vehicles. Due to its communication capabilities, the controller area network (CAN) is the most widely used in-vehicle communication protocol. However, CAN lacks suitable security mechanisms such as message authentication and encryption. This makes the CAN bus vulnerable to numerous cyberattacks. Not only are these attacks a threat the information security and privacy, but they can also directly affect the safety of drivers, passengers and the surrounding environment of the moving vehicles. This paper presents CAN-CID, a context-aware intrusion detection system (IDS) to detect cyberattacks on the CAN bus, which would be suitable for deployment in automobiles including military vehicles, passenger cars, commercial vehicles and other CAN-based applications such as aerospace, industrial automation and medical equipment. CAN-CID is an ensemble model of a gated recurrent unit (GRU) network and a time-based model. A GRU algorithm works by learning to predict the centre ID of a CAN ID sequence, and ID-based probabilistic thresholds are used to identify anomalous IDs, whereas the time-based model identifies anomalous IDs using time-based thresholds. The number of anomalies compared to the total number of IDs over an observation window is used to classify the window status as anomalous or benign. The proposed model uses only benign data for training and threshold estimation, avoiding the need to collect realistic attack data to train the algorithm. The performance of the CAN-CID model was tested against three datasets over a range of 16 attacks, including fabrication and more sophisticated masquerade attacks. The CAN-CID model achieved an F1-Score of over 99% for 13 of those attacks and outperformed benchmark models from the literature for all attacks, with near real-time detection latency.

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