IJSRP, Volume 6, Issue 10, October 2016 Edition [ISSN 2250-3153]
K.R.W.V.Bandara, T.S.Abeysinghe, A.J.M.Hijaz, D.G.T.Darshana, H.Aneez, , S.J.Kaluarachchi,K.V.D.L.Sulochana and Mr.DhishanDhammearatchi
Distributed denials of service (DDOS) attack have strong impact on the cyber world. As far as cyber-attack is concerned that it halts the normal functioning of the organization by Internet protocol (IP) spoofing, bandwidth overflow, consuming memory resources and causes a huge loss. There has been a lot of related work which focused on analyzing the pattern of the DDOS attacks to protect users from them. A User datagram protocol (UDP) flood is a network flood and still one of the most crucial network floods today. This paper presents a comprehensive survey of preventing DDOS attack recognize by data mining techniques with the use of identifying DDOS attack patterns and analyze patterns by machine learning algorithms. There are some leading machine learning algorithms used to recognize the DDOS attack such as k-Nearest Neighbors algorithm (KNN), support vector machines (SVM), Random Forest as well as Naïve Base. The paper also highlights open issues, research challenges and possible solutions in this area. The result shows the highest accuracy rate of preventing DDOS attack recognizing by data mining algorithms.
K.R.W.V.Bandara, T.S.Abeysinghe, A.J.M.Hijaz, D.G.T.Darshana, H.Aneez, , S.J.Kaluarachchi,K.V.D.L.Sulochana and Mr.DhishanDhammearatchi (2018); Preventing DDoS attack using Data mining Algorithms;
Int J Sci Res Publ 6(10) (ISSN: 2250-3153). http://www.ijsrp.org/research-paper-1016.php?rp=P585918