IPv6 DoS Attacks Detection Using Machine Learning Enhanced IDS in SDN/NFV Environment
- Resource Type
- Conference
- Authors
- Tseng, Chia-Wei; Wu, Li-Fan; Hsu, Shih-Chun; Yu, Sheng-Wang
- Source
- 2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS) Network Operations and Management Symposium (APNOMS), 2020 21st Asia-Pacific. :263-266 Sep, 2020
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Security
Communication networks
Machine learning
Network function virtualization
Training
Systems architecture
Intrusion detection
IPv6
Traffic Classification
Machine Learning
Decision Tree
IDS
- Language
The rapid growth of IPv6 traffic makes security issues become more important. This paper proposes an IPv6 network security system that integrates signature-based Intrusion Detection Systems (IDS) and machine learning classification technologies to improve the accuracy of IPv6 denial-of-service (DoS) attacks detection. In addition, this paper has also enhanced IPv6 network security defense capabilities through software-defined networking (SDN) and network function virtualization (NFV) technologies. The experimental results prove that the detection and defense mechanisms proposed in this paper can effectively strengthen IPv6 network security.