A Ddos Attack Categorization and Prediction Method Based on Machine Learning

Main Article Content

S.Siva saravanababu
G.Saravanakumar
Naveen V M
Ajitesh kumar A S B
Koushik P H
Carolyne Sneha
Bhuvaneswari

Keywords

DDOSattack, KNNAlgorithm, DNNAlgorithm, Standard Scalar, Confusionmatrix, KNNclassifier technique, DNN classifier approach

Abstract

The most popular term for distributed network attacks is distributed denial of service (DDoS) attacks. These attacks employ certain limitations imposed on each arrangement asset, such as the design of the authorised organisation site. In this research, it is suggested that DDoS attack types be classified and foreseen using machine learning. The classification algorithms KNN and DNN are employed in this project's work. StandardScaler is used to pre-process the datasets. After using StandardScaler to remove the mean, the data are scaled to the unit variance. An evaluation of the model's performance was done using the confusion matrix created by the proposed project. For both Precision (PR) and Recall in the first classification, the KNN classifier technique is utilised (RE). The second classification makes use of the DNN classifier approach

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