Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4956961 | Optical Fiber Technology | 2017 | 6 Pages |
Abstract
In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Networks and Communications
Authors
Huibin Zhang, Yuqiao Wang, Haoran Chen, Yongli Zhao, Jie Zhang,