Name
Abstract | ||
|---|---|---|
AbstractIn order to detect abnormal communication behaviors efficiently in today's industrial control system, a new intrusion detection algorithm based on One-Class Support Vector Machine OCSVM is proposed in this paper. In this algorithm, a normal communication behavior model is established by using OCSVM, and the Particle Swarm Optimization algorithm is designed to optimize OCSVM model parameters. Furthermore, we adopt the normal Modbus function code sequence to train OCSVM model, and then use this model to detect abnormal Modbus TCP traffic. Our simulation results show that the proposed algorithm not only is efficient and reliable but also meets the real-time requirements of anomaly detection in industrial control system. Copyright © 2015 John Wiley & Sons, Ltd. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1002/sec.1398 | Periodicals |
Keywords | Field | DocType |
SVM,intrusion detection,PSO | Particle swarm optimization,Anomaly detection,Computer science,Computer security,Support vector machine,Algorithm,Real-time computing,Industrial control system,Function Code,Modbus,Intrusion detection system | Journal |
Volume | Issue | ISSN |
9 | 10 | 1939-0114 |
Citations | PageRank | References |
8 | 0.52 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenli Shang | 1 | 25 | 7.07 |
| ZENG Peng | 2 | 31 | 11.10 |
| Ming Wan | 3 | 20 | 3.43 |
| Lin Li | 4 | 8 | 0.52 |
| Panfeng An | 5 | 8 | 0.52 |