A Survey on Machine Learning Techniques used for Detection of DDOS Attacks
5 Pages Posted: 24 Dec 2019
Date Written: May 17, 2019
Distributed Denial of service (DDOS) attack is one of the most annihilating attack which adheres the functionality of basic administrations given by the different firms in the internet. These attacks have turned out to be progressively complex and keep on expanding in number step by step, in this way making it hard to distinguish and to counter such attacks. In this way there is a need of keen intrusion detection system (IDS) to recognize and arrange any irregular conduct of the system traffic. In a DDOS attack, the intruder takes the advantage of the known or unknown flaws and vulnerabilities by using zombies (innocent compromised PCs) and sends a large number of packets from these zombies to the server. This takes the network bandwidth of the user and halts the services which the user was using. Hence the detection of DDOS attack is very critical .In this paper, a survey is made on various machine learning techniques used to detect the DDOS attack.
Keywords: DDOS attack, Cyber Security, Decision tree, Naïve Bayes, Support Vector Machine, Neural Network and Fuzzy Logic, Machine Learning(ML)
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