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Intelligent Intrusion Detection Framework for Anomaly-Based CAN Bus Network Using Bidirectional Long Short-Term Memory
The modern vehicle depends on controller area network (CAN) bus technology to facilitate communication among electronic control units. Although the CAN bus technology is significant, it lacks the typical authentication and authorization protocols, allowing interactions susceptible to unauthorized access. Attackers can easily target the CAN bus network because of this vulnerability. Attackers have several options when it comes to infiltrating a CAN bus system, and several of them are focused on either denial of service, fuzzing, or spoofing attacks. To ensure current autos are secure, it is critical to developing ways to guard against these threats. In order to identify and eliminate vulnerabilities affecting CAN bus networks, prior research has developed several works, including intrusion detection systems (IDSs) based on (ML) and deep learning (DL) approaches such as the long short-term memory (LSTM)-based IDS. Many previous studies employed ML or DL methodologies to identify and mitigate CAN bus network assaults. Technological advancements increase attack variety. An IDS has been designed for the CAN bus that is more precise and accurate. CAN bus network assaults are being detected and mitigated in this study using a Bidirectional_LSTM-based IDS. Using the suggested framework, non-attack and attack records can be discriminated and precisely characterized. With improved accuracy and an intelligent IDS creating process, the suggested model outperforms other standard ML approaches. The proposed method outperforms other competitive methods with an accuracy of 99.09% and signifies the efficacy of the developed method.
Intelligent Intrusion Detection Framework for Anomaly-Based CAN Bus Network Using Bidirectional Long Short-Term Memory
The modern vehicle depends on controller area network (CAN) bus technology to facilitate communication among electronic control units. Although the CAN bus technology is significant, it lacks the typical authentication and authorization protocols, allowing interactions susceptible to unauthorized access. Attackers can easily target the CAN bus network because of this vulnerability. Attackers have several options when it comes to infiltrating a CAN bus system, and several of them are focused on either denial of service, fuzzing, or spoofing attacks. To ensure current autos are secure, it is critical to developing ways to guard against these threats. In order to identify and eliminate vulnerabilities affecting CAN bus networks, prior research has developed several works, including intrusion detection systems (IDSs) based on (ML) and deep learning (DL) approaches such as the long short-term memory (LSTM)-based IDS. Many previous studies employed ML or DL methodologies to identify and mitigate CAN bus network assaults. Technological advancements increase attack variety. An IDS has been designed for the CAN bus that is more precise and accurate. CAN bus network assaults are being detected and mitigated in this study using a Bidirectional_LSTM-based IDS. Using the suggested framework, non-attack and attack records can be discriminated and precisely characterized. With improved accuracy and an intelligent IDS creating process, the suggested model outperforms other standard ML approaches. The proposed method outperforms other competitive methods with an accuracy of 99.09% and signifies the efficacy of the developed method.
Intelligent Intrusion Detection Framework for Anomaly-Based CAN Bus Network Using Bidirectional Long Short-Term Memory
J. Inst. Eng. India Ser. B
Kishore, Ch. Ravi (author) / Rao, D. Chandrasekhar (author) / Nayak, Janmenjoy (author) / Behera, H. S. (author)
Journal of The Institution of Engineers (India): Series B ; 105 ; 541-564
2024-06-01
24 pages
Article (Journal)
Electronic Resource
English
ECU , CAN bus , IDS , LSTM , B_LSTM , DL , ML Engineering , Communications Engineering, Networks