Affiliation:
1. Acharya Nagarjuna University
2. QIS College of Engineering and Technology
Abstract
In today's world, the banking sector, government organizations, and various users in the finance and insurance sectors have grown exponentially. In such situations, they become primary targets for attackers. The main focus of these attackers is to disrupt services for legitimate users. Recently, attackers have targeted banks in Ukraine during the Russia- Ukraine war, causing a shortage of money in banks and making it difficult for people to withdraw funds. These types of attacks fall under the category of Distributed Denial of Service (DDoS) attacks. The primary objectives of these DDoS attacks are to gain financial control and damage the reputation of the affected organization or country. The purpose of this paper is to detect DDoS attacks using various Decision Tree Classifiers in Machine Learning algorithms. We utilized the 'caret' package in R, which is well-known for its Classification and Regression Techniques. We split the KDD'99 dataset based on the outcome variable. We employed the 'rpart' method to classify the dataset using CART and C4.5 algorithms. Experimental results indicate that our classification methods achieve a better accuracy rate compared to other decision tree methods.
Reference21 articles.
1. Multilevel Deep Neural Network Approach for Enhanced Distributed Denial-of-Service Attack Detection and Classification in Software-Defined Internet of Things Networks
2. Distributed Denial of Service Attack Detection for the Internet of Things Using Hybrid Deep Learning Model
3. Analytics Vidhya. (2024). Generative AI Pinnacle
Program: Build LLM Models from Scratch. Retrieved from http://www.analyticsvidhya.com
4. Bouzida, Y., Cuppens, F., Cuppens-Boulahia, N., &
Gombault, S. (2004, June). Efficient intrusion detection
using principal component analysis. In 3rd Conference on
Security and Network Architectures (SAR) (pp. 381-395).
5. Gadallah, W. G., Omar, N. M., & Ibrahim, H. M. (2021).
Machine Learning-based distributed denial of service
attacks detection technique using new features in
software-defined networks. International Journal of
Computer Network & Information Security, 13(3).