A deep learning-based intrusion detection system for in-vehicle networks. (December 2022)

• Record Type: Journal Article
• Authors: Alqahtani, Hamed; Kumar, Gulshan
• Is Part Of: Computers & electrical engineering. Volume 104:Part B(2022)
• Publication Date: 2022-12
• Subjects: Automotive security -- Controller area network -- Convolutional neural network -- Intrusion detection -- In-vehicle network -- Long–short term memory network -- Representation learning -- Security and privacy; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108447
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Modern vehicles are increasingly getting connected within the vehicles, with other systems, leading to more concerns about security. Controller area network (CAN) has become a de-facto standard for connecting internal vehicles' components. However, it lacks security features. Conventional security mechanisms fail to protect in-vehicle networks from attacks, requiring the development of an effective intrusion detection system (IDS). This work develops an IDS for in-vehicle networks called IDS-IVN based on a compact representation of location invariant and time-variant traffic features using deep learning. The IDS-IVN uses convolutional neural and long–short-term memory networks as encoder/decoder functions of autoencoder networks to extract features from raw data and classify them using latent space representation into intrusive and non-intrusive classes. A benchmark real-time ROAD dataset is used to demonstrate the IDS-IVN's performance compared to the existing methods. IDS-IVN reports 99% accuracy with a 0.32% low false-positive rate for detecting intrusions.