Paper Title
Standard Scaling and PCA based KNN Model for Detecting and Classifying DDOS Attack

Abstract
A distributed denial-of-service (DDoS) attack is a malicious way to disrupt the regular traffic coming towards the server which is targeted by using the flood of unuseful traffic so that the targeted server will not respond to real traffic anymore and, as a result of this, the server stops responding. In this, the hacker user not only a single computer (bot) but uses a group of computers (bots). This type of malicious traffic is created in many ways, and one of the most prominent ways is to use the bots, botnet (a group of bots). These bots continuously generate unuseful traffic at a higher rate so that real traffic is left behind. As a result, the server will deny the new valid connections. This denial is defined as a denial of service. To overcome this, it's required for the server to make a distinction between actual traffic and malicious traffic and then stop responding to the malicious traffic. To detect this DDoS traffic, we propose a KNN (K-Nearest Neighbours) Model, which is used to detect the type of attack performed on the system. For this proposed work, we will use the UNWS-np-15 dataset and enhance the detection accuracy. Keywords - DDOS, ML, KNN

Author - Avinash Gupta, Anoj Kumar

Published : Volume-10,Issue-2  ( Feb, 2023 )

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