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An Anomaly Detection Method Based on Multiple LSTM-Autoencoder Models for In-Vehicle Network
Open accessAbstract
The CAN (Controller Area Network) protocol is widely adopted for in-vehicle networks due to its cost efficiency and reliable transmission. However, despite its popularity, the protocol lacks built-in security mechanisms, making it vulnerable to attacks such as flooding, fuzzing, and DoS. These attacks can exploit vulnerabilities and disrupt the expected behavior of the in-vehicle network. One of the main reasons for these security concerns is that the protocol relies on broadcast frames for communication between ECUs (Electronic Control Units) within the network. To tackle this issue, we present an intrusion detection system that leverages multiple LSTM-Autoencoders. The proposed system utilizes diverse features, including transmission interval and payload value changes, to capture various characteristics of normal network behavior. The system effectively detects anomalies by analyzing different types of features separately using the LSTM-Autoencoder model. In our evaluation, we conducted experiments using real vehicle network traffic, and the results demonstrated the system’s high precision with a 99% detection rate in identifying anomalies.
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References 28 · Cited by 28
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Metadata
- Authors
- Kim, T., Kim, J., & You, I.
- Affiliations
- Department of Information Security Engineering, Soonchunhyang University, Asan 31538, Republic of Korea
- School of Computer Science, Gyeongsang National University, Jinju 52828, Republic of Korea
- Kookmin University (KR)
- Corresponding author
- You, Ilsun
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2023-08-22
- Volume
- 12
- Issue
- 17
- Pages
- 3543
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4386070297
- Cited by
- 28
- References
- 28
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 8 · 2023: 3
- FWCI
- 3.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- Funders
- Soonchunhyang University
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
- Keywords
- anomaly detection, in-vehicle networks, CAN bus, LSTM autoencoder, intrusion detection, network security, flooding attacks, denial-of-service attacks, broadcast traffic, electronic control units
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