Application Layer DDoS Attack Defense Methods with a New Method against Flooding
Sreeja Nair M. P.1, Mathew Cherian2, Preetha Mathew K.3
1Sreeja Nair M. P.*, Faculty and Research Scholar, Cochin University College of Engineering Kuttanad, Cochin University of Science and Technology, Kerala, India.
2Dr. Mathew Cherian, Department of Mechanical Engineering, Cochin University College of Engineering Kuttanad, Cochin University of Science and Technology, Kerala, India.
3Dr. Preetha Mathew K., Faculty and Research Scholar, Cochin University College of Engineering Kuttanad, Cochin University of Science and Technology, Kerala, India.
Manuscript received on February 12, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 30, 2020. | PP: 208-212 | Volume-8 Issue-6, March 2020. | Retrieval Number: F6986038620/2020©BEIESP | DOI: 10.35940/ijrte.F6986.038620
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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: In a network environment, Distributed Denial of Service (DDoS) attacks eemploys a network or server is unavailable to its normal users. Application-layer Distributed Denial of Service (App-DDoS) attacks are serious issues for the webserver itself. The multitude and variety of such attacks and defense approaches are overwhelming. This paper here follows, we analyze the different defense mechanisms for application-layer DDoS attacks and proposes a new approach to defend using machine learning.
Keywords: Application Layer-DDOS Attacks, Defense.
Scope of the Article: Probabilistic Models and Methods.