DDOS ATTACK CLASSIFICATION AND PREDICTION METHOD USING MACHINE LEARNING
Author(s):
M. Evangeline Mercy, Dr. I. Felcia Jerlin
Keywords:
PR-Precision, RE-Recall, DDoS-Distributed Denial of Service.
Abstract
In general, distributed network attacks are referred to as Distributed Denial of Service (DDoS) attacks. Attacks like this capitalize on of unique constraints that apply to every arrangement asset, such as the authorized organization site's framework. This research provides a machine learning approach for classifying and predicting DDoS attacks. In this research, the classification algorithms LR, KNN, and Decision Tree are used. The datasets are pre-processed using Standard Scalar. The mean is removed and the data is scaled to the unit variance using Standard Scalar. This suggested project created a confusion matrix to determine the performance of the model. In the initial categorization, the Logistic Regression classifier technique is used for both Precision (PR) and Recall (RE). In the second classification, the KNN classifier method is utilized to overcome the difficulties and determine the accuracy, precision, and confusion matrix. The decision tree has the substantial advantage of requiring all possible outcomes of a decision to be considered and following each path to a conclusion. Python software is used to carry out this project.
Article Details
Unique Paper ID: 161218

Publication Volume & Issue: Volume 10, Issue 3

Page(s): 33 - 42
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