Open AccessAn investigation of different DDOS attack detection methods in software-defined networks
Author(s) -
Gaganjot Kaur,
Phalguni Gupta
Publication year - 2022
Publication title -
international journal of health sciences (ijhs) (en línea)
Language(s) - English
Resource type - Journals
eISSN - 2550-6978
pISSN - 2550-696X
DOI - 10.53730/ijhs.v6ns1.4863
Subject(s) - denial of service attack , computer science , software , network security , computer security , application layer ddos attack , software defined networking , data mining , machine learning , artificial intelligence , computer network , the internet , world wide web , operating system
Software-Defined Network is more vulnerable to more frequent and severe security attacks. Distributed Denial of service (DDoS) spasms corrupt network along with hinder efficiency and performance significantly. DDoS spasms lead to exhaustion of network means, thereby stopping the controller and impeding normal activities. Detection of DDoS attacks requires different classification techniques that provide accurate and efficient decision-making. Various techniques to detect the attacks are proposed in the existing literature. However, analysis of various works reveals various shortcomings of different techniques. In this paper, the existing techniques are analyzed in terms of their accuracy and MSE, and seven methods are compared with regards to suitability to counter DDoS attacks efficiently. Analysis of the results shows limitations and sets the tone for future studies on the topic. Overall, it is suggested to continue looking for better techniques to improve upon the existing learning and experiences gained and provide more accurate results.