The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
CANintelliIDS: Detecting In-Vehicle Intrusion Attacks on a Controller Area Network Using CNN and Attention-Based GRU
Abstract
Controller area network (CAN) is a communication protocol that provides reliable and productive transmission between in-vehicle nodes continuously. CAN bus protocol is broadly utilized standard channel to deliver sequential communications between electronic control units (ECUs) due to simple and reliable in-vehicle communication. Existing studies report how easily an attack can be performed on the CAN bus of in-vehicle due to weak security mechanisms that could lead to system malfunctions. Hence the security of communications inside a vehicle is a latent problem. In this paper, we propose a novel approach named CANintelliIDS, for vehicle intrusion attack detection on the CAN bus. CANintelliIDS is based on a combination of convolutional neural network (CNN) and attention-based gated recurrent unit (GRU) model to detect single intrusion attacks as well as mixed intrusion attacks on a CAN bus. The proposed CANintelliIDS model is evaluated extensively and it achieved a performance gain of 10.79% on test intrusion attacks over existing approaches.
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In-vehicle network intrusion detection using deep convolutional neural network
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Introduction to Industrial Control Networks
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Self-citationA Practical Wireless Attack on the Connected Car and Security Protocol for In-Vehicle CAN
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Cited by
Federated Learning for intrusion detection system: Concepts, challenges and future directions
Agrawal, S., Sarkar, S., Aouedi, O., Yenduri, G., Piamrat, K., Alazab, M., Bhattacharya, S., Maddikunta, P. K. R., & Gadekallu, T. R. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.09.012 · Cited by 457
Self-citationCyber Security in IoT-Based Cloud Computing: A Comprehensive Survey
Ahmad, W., Rasool, A., Javed, A. R., Baker, T., & Jalil, Z. (2021) · Electronics · doi:10.3390/electronics11010016 · Cited by 268
Self-citationSurvey on artificial intelligence (AI) techniques for vehicular ad-hoc networks (VANETs)
Mchergui, A., Moulahi, T., & Zeadally, S. (2022) · Vehicular Communications · doi:10.1016/j.vehcom.2021.100403 · Cited by 165
Kumar, P., Kumar, R., Kumar, A., Franklin, A. A., Garg, S., & Singh, S. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3191601 · Cited by 117
RetractedStacked one-class broad learning system for intrusion detection in industry 4.0
Yang, K., Shi, Y., Yu, Z., Yang, Q., Sangaiah, A. K., & Zeng, H. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3157727 · Cited by 75
Metadata
- Authors
- Javed, A. R., Rehman, S. U., Khan, M. U., Alazab, M., & G, T. R.
- Venue
- IEEE Transactions on Network Science and Engineering
- Published
- 2021-04-01
- Volume
- 8
- Issue
- 2
- Pages
- 1456-1466
- ISSN
- 2327-4697 (ISSN-L)
- 2334-329X
- OpenAlex
- W3130982961
- Cited by
- 331
- References
- 27
- FWCI
- 27.87
- Type
- journal article
- Language
- en
- Open access
- closed
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- Javed, A. R., Rehman, S. U., Khan, M. U., Alazab, M., & G, T. R. (2021). CANintelliIDS: Detecting In-Vehicle Intrusion Attacks on a Controller Area Network Using CNN and Attention-Based GRU. IEEE Transactions on Network Science and Engineering. https://doi.org/10.1109/tnse.2021.3059881
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