Abstract
In online Services, Distributed Denial of Service (DDoS) remains as one of the main threats. Attackers can execute DDoS by the steps which is easier and with the high efficiency, to slow down services for the user’s access. To detect the DDoS attack, machine learning algorithms are used. The supervised machine learning algorithms like Naive Bayes, decision tree, k-nearest neighbors (k-NN) and random forest, are used for detection and mitigation of attack. There are three steps: information collecting, Preprocessing and feature extraction in the classification algorithm for detection of “Normal or DDoS” attack using the NSL-KDD dataset. Different algorithms exhibit different behavior based on the selected features. The performance of DDOS attack detection is compared and best algorithm is suggested.
Published Version
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