TOW-IDS: Intrusion detection system based on three overlapped wavelets for automotive ethernet
Abstract
Devices that ensure vehicle and driver safety or provide services to drivers generate a substantial amount of network traffic. The traffic is transmitted to the In-Vehicle Network (IVN) depending on the defined function. Consequently, to quickly process a lot of traffic transmitted to the IVN, an advanced network protocol such as Automotive Ethernet is necessary. However, owing to the connectivity reinforcement between devices inside a vehicle and external networks, attack vectors and vulnerabilities can be easily inherited from an established Ethernet to Automotive Ethernet. The present study proposes a method for detecting and identifying abnormalities in Automotive Ethernet based on wavelet transform and deep convolutional neural network. First, we define attack scenarios and extract normal and abnormal data corresponding to these scenarios. Second, we conduct several preprocesses, such as fixing the packet size and normalizing the network image data. Finally, we conduct extensive evaluations of the proposed method’s performance, considering the size of network image data and multi-resolution levels. The results demonstrate that the proposed method can effectively detect an abnormality. Furthermore, the results suggest that the our method is more effective in terms of time-cost compared to default ResNet and EfficientNet methods.
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The dataset distribution for time-series dependency-based techniques reveals a concentration on a limited set of benchmarks, with CIC-IDS2017, UNSW-NB15, IoT-23, LITNET-2020, NetML-2020, CIC-DDoS2019, and N-BaIoT widely used, especially for botnet, DDoS, and generic network intrusions, while only a subset of studies leverage ICS/CPS-specific traces such as Alstom TCMS traffic, multi-domain ICS-CPS
“Fourth, the dataset distribution again reveals a concentration on a limited set of benchmarks. CIC-IDS2017, UNSW-NB15, IoT-23, LITNET-2020, NetML-2020, CIC-DDoS2019, and N-BaIoT are widely used to evaluate time-series dependency-based techniques, especially for botnet, DDoS, and generic network intrusions [35,38,40,43]. Only a subset of studies leverage ICS/CPS-specific traces such as Alstom TCMS”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [43]
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- Authors
- Han, M. L., Kwak, B. I., & Kim, H. K.
- Affiliations
- Korea University (KR)
- Hallym University (KR)
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2023
- Volume
- 18
- Pages
- 411-422
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W4311080744
- Cited by
- 58
- References
- 34
- Citations by year
- 2026: 14 · 2025: 27 · 2024: 10 · 2023: 7
- FWCI
- 4.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- Funders
- Institute of Information & communications Technology Planning & Evaluation
- Korea government [Ministry of Science and ICT (MSIT)] (2021-0-00903)
- Development of Physical Channel Vulnerability-based Attacks and its Countermeasures for Reliable On-Device Deep Learning Accelerator Design
- National Research Foundation of Korea
- Korea government (2022R1A4A1033600)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- intrusion detection, automotive Ethernet, in-vehicle networks, wavelet transform, convolutional neural networks, network traffic anomaly detection, attack scenarios, automotive network security
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