Feature Selection Techniques Cloud DDOS Attack Detection
S.Emerald Jenifer Mary1, C.Nalini2
1S.Emerald Jenifer Mary*, Research Scholar, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India.
2C.Nalini, Professor, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 25 September, 2019. | Manuscript published on October 10, 2019. | PP: 1257-1260 | Volume-8 Issue-12, October 2019. | Retrieval Number: L39081081219/2019©BEIESP | DOI: 10.35940/ijitee.L3908.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The ongoing progression of Cloud Computing, it gives different services to together hierarchical as well as singular users, for example, shared computing resources, storage, networking and so on interest. The most well-known sort of attack on Cloud-computing is Distributed Denial of Service- (DDoS) Attack. DDoS attack is an bother which makes resources inaccessible to the client by trading off enormous no of system called bots. This paper proposes systems to create an ideal network traffic feature set for network intrusion detection. The proposed system shows that a reliable set of features are chosen for a given dataset. The outcomes demonstrate that the proposed procedure yields a set of features that, when utilized for network traffic classification, yields low quantities of false alarms.
Keywords: Cloud, Feature Section, Machine learning, Attack Detection, Ddos attack.
Scope of the Article: Machine learning