Volume 15, Number 3

DDoS Attacks Detection using Dynamic Entropy in Software-Defined Network Practical Environment


Dinh Thi Thai Mai, Nguyen Tien Dat, Pham Minh Bao, Can Quang Truong, Nguyen Thanh Tung, VNU University of Engineering and Technology, Hanoi, Vietnam


Software-Defined Network (SDN) is an innovative network architecture with the goal of providing the flexibility and simplicity in network operation and management through a centralized controller. These features help SDN to easily adapt tothe expansion of networkrequirements, but it is also a weakness when it comes to security. With centralized architecture, SDN is vulnerable to cyber-attacks, especially Distributed Denial of Service (DDoS) attack. DDoS is a popular attack type which consumes all network resources and causes congestion in the entire network. In this research, we will introduce a DDoS detection model based on the statistical method with a dynamic threshold value that changes over time. Along with the simulation result, we build a practical SDN model to apply our method, the results show that our method can detectDDoS attacks rapidly with high accuracy.


SDN, DDoS attacks, network security, machine learning, statistical analysis method, entropy, dynamic entropy.