A Frequency-based Data Mining Approach to Enhance in-vehicle Network Intrusion Detection
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20219033
- Paper/Info type
- Other International Conferences
- Pages
- 1-6(Total 6 p)
- Date of publication
- Sep 2021
- Publisher
- JSAE
- Language
- English
Detailed Information
| Author(E) | 1) Joakim Rosell, 2) Cristofer Englund, 3) Arash Vahidi, 4) Nishat I Mowla, 5) Ana Magazinius, 6) Eric Järpe |
|---|---|
| Affiliation(E) | 1) Department of Mobility and Systems, 2) Department of Mobility and Systems;Halmstad University, 3) Research Institutes of Sweden, 4) Department of Mobility and Systems, 5) Department of Mobility and Systems, 6) Halmstad University |
| Abstract(E) | Modern vehicles have numerous electronic control units (ECUs) that constantly communicate over embedded in-vehicle networks (IVNs) comprised of controlled area network (CAN) segments. The simplicity and size-constrained 8-byte payload of the CAN bus technology makes it infeasible to integrate authenticity and integrity-based protection mechanisms. Thus, a malicious component will be able to inject malicious data into the network with minimal risk for detection. Such vulnerabilities have been demonstrated with various security attacks such as the flooding, fuzzing, and malfunction attacks. A practical approach to improve security in modern vehicles is to monitor the CAN bus traffic to detect anomalies. However, to administer such an intrusion detection system (IDS) with a general approach faces some challenges. First, the proprietary encodings of the CAN data fields need to be omitted as they are intellectual property of the original equipment manufacturers (OEMs) and differ across vehicle manufacturers and their models. Secondly, such general and practical IDS approach must also be computationally efficient in terms of speed and accuracy. Traditional IDSs for computer networks generally utilize a rule or signature-based approach. More recently, the approach of using machine learning (ML) with efficient feature representation has shown significant success because of faster detection and lower development and maintenance costs. Therefore, an efficient data aggregation technique with enhanced frequency-based feature representation to improve the performance of MLbased IDS for the IVNs is proposed. The performance gain was verified with the Survival Analysis Dataset for automobile IDS. |
