| Title | IPv6 DoS Attacks Detection Using Machine Learning Enhanced IDS in SDN/NFV Environment |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Tseng, Chia-Wei, Wu, Li-Fan, Hsu, Shih-Chun, Yu, Sheng-Wang |
| Conference Name | 2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS) |
| Keywords | Communication networks, composability, Decision Tree, IDS, Intrusion detection, IPv6, ipv6 security, machine learning, Metrics, network function virtualization, policy-based governance, pubcrawl, Resiliency, Scalability, security, signature based defense, Systems architecture, Traffic classification, Training |
| Abstract | 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. |
| DOI | 10.23919/APNOMS50412.2020.9237056 |
| Citation Key | tseng_ipv6_2020 |
IPv6 DoS Attacks Detection Using Machine Learning Enhanced IDS in SDN/NFV Environment