Prediction of DDoS Attacks Using DecisionTree Technique

EasyChair Preprint 9235

Abstract

As a result of cloud computing in previous years, the fields of computing and information technology have been subjected to a massive change in industry. Customers have been given the capacity, as a result of this capability, to rent virtual resources and utilize a wide variety of on-demand services at the most cost-effective prices. The use of cloud computing comes with a lot of advantages; yet, it is also predictable for a variety of threats. One of these threats is a distributed denial of service attack, also known as a DDoS attack, which is considered to be one of the most deadly varieties of cyberattack. In this essay, we will examine the prediction of distributed denial of service attacks using decision tree algorithm more accurately. A subset of data collected by the Canadian Institute for Cybersecurity is used. The dataset contains attacks that can be executed using TCP and UDP-based protocols has been taken, The project aims to extract knowledge from data for the classification of DDoS attacks and predict the DDoS attacks using decision tree technique.

Keyphrases: DDoS attacks, Decision Tree, Predictive Analytics, Security, Threats, machine learning

@booklet{EasyChair:9235,
  author    = {A Venkata Dineshreddy and Chandana Vamshi Krishna and V Lavanya},
  title     = {Prediction of  DDoS Attacks Using DecisionTree Technique},
  howpublished = {EasyChair Preprint 9235},
  year      = {EasyChair, 2022}}