Unsupervised learning with hierarchical feature selection for DDoS mitigation within the ISP domain

• Main Authors: Ili Ko, Desmond Chambers, Enda Barrett
• Format: Article
• Language: English
• Published: Electronics and Telecommunications Research Institute (ETRI) 2019-09-01
• Series: ETRI Journal
• Subjects: ANN; artificial neural network; cyber security; DDoS mitigation; feature selection; self‐organizing map; unsupervised learning
• Online Access: https://doi.org/10.4218/etrij.2019-0109

A new Mirai variant found recently was equipped with a dynamic update ability, which increases the level of difficulty for DDoS mitigation. Continuous development of 5G technology and an increasing number of Internet of Things (IoT) devices connected to the network pose serious threats to cyber security. Therefore, researchers have tried to develop better DDoS mitigation systems. However, the majority of the existing models provide centralized solutions either by deploying the system with additional servers at the host site, on the cloud, or at third party locations, which may cause latency. Since Internet service providers (ISP) are links between the internet and users, deploying the defense system within the ISP domain is the panacea for delivering an efficient solution. To cope with the dynamic nature of the new DDoS attacks, we utilized an unsupervised artificial neural network to develop a hierarchical two‐layered self‐organizing map equipped with a twofold feature selection for DDoS mitigation within the ISP domain.