Detection, Defensive and Mitigation of DDoS Attacks through Machine learning Techniques: A Literature
P Krishna Kishore1, S Ramamoorthy2, V. N Rajavarman3
1P Krishna Kishore1, Research Scholar, Dr. M.G.R Educational and Research Institute, Chennai, India.
2Dr. S Ramamoorthy2,Professor & Dean MCA, Dr. M.G.R Educational and Research Institute, Chennai, India.
3Dr.V.N Rajavarman3, Professor & Deputy Dean, Dr. M.G.R Educational and Research Institute, Chennai, India. 

Manuscript received on November 22, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on November 30, 2019. | PP: 2719-2725 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7335118419/2019©BEIESP | DOI: 10.35940/ijrte.D7335.118419

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Abstract: Nowaday the world is completely depend on the internet and the day to day activities of human life completely depends on the internet. The dependency ion the iinternet iallows ithe iattackers ido damage ior iharm ito ithe legitimate user’s transactions and events which is called is Security attack. Distributed Denial of Service is one itype iof ithe imost vulnerable iattacks iof itoday’s icyber iworld. iIn ithis ipaper, iwe ipresent ia isurvey iof iDistributed iDenial iof iService iattack, idetection, idefensive iand imitigation iof imachine ilearning iapproaches. This isurvey iarticle iprefer itwo ifamous isupervised imachine ilearning ialgorithms isnamely. (i) Decision itrees, (ii)isupport ivector imachine and ipresented ithe irecent iresearch iworks icarried iout. From ithis isurvey iit iis ilearnt ithat iconnecting supervised imachine ilearning ialgorithm iwith iboosting iprocess will iincrease iprediction iefficiency iand ithere iis ia iwide iscope iin ithis iresearch ielement. We provide a systematic analysis of these attacks including so many motivations and evolutions, different types of attacks analysis so far, detection techniques and mitigation techniques, possible constraints and challenges of existing approaches. Finally some important research points are outlined to ensure successful detection, defensive and mitigation against Distributed Denial of Service attacks.
Keywords: DoS attack, Distributed Denial of service attack, Detection, Defensive, Mitigation, Machine Learning Approaches.
Scope of the Article: Machine Learning.