Semi-Supervised Machine Learning Approaches for DDOS Attack Detection

Gopu Chitra Bhanu Reddy; Dude Srikanth; Jakkidi Santhosh Reddy; V Narasimha

doi:10.36948/ijfmr.2023.v05i06.9272

Semi-Supervised Machine Learning Approaches for DDOS Attack Detection

Author(s)	Gopu Chitra Bhanu Reddy, Dude Srikanth, Jakkidi Santhosh Reddy, V Narasimha
Country	India
Abstract	Network infrastructures are the target of several attacks. These include intrusions into the confidentiality, integrity, and availability of the network. The network's availability is impacted by a persistent attack known as a distributed denial-of-service (DDoS) attack. Such an assault is carried out using a command and control (C & C) technique. To detect these assaults, numerous researchers have put forth various machine learning-based solutions. In this paper, we are going to detect different DDoS attacks by various methods and evaluate their performance. This experiment made use of the KD99 dataset. The normal and assault samples were classified using the random forest technique. The classification of 99.76% of the samples was accurate. By strategically selecting clusters and incorporating the insights gained from the small labelled dataset, a portion of the unlabelled clusters can be assigned labels, effectively converting raw data into useful training examples. This enriched dataset is then used to train an improved classifier that can better generalize and adapt to the dynamic nature of DDoS attacks.
Keywords	DDoS Attack Detection, Machine Learning, Cybersecurity, Semi-Supervised Learning, Model Selection, Performance Evaluation
Field	Physical Science
Published In	Volume 5, Issue 6, November-December 2023
Published On	2023-11-25
Cite This	Semi-Supervised Machine Learning Approaches for DDOS Attack Detection - Gopu Chitra Bhanu Reddy, Dude Srikanth, Jakkidi Santhosh Reddy, V Narasimha - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.9272
DOI	https://doi.org/10.36948/ijfmr.2023.v05i06.9272
Short DOI	https://doi.org/gs63xd

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 4 Cover Page Vol 6 Isu 3 Cover Page Vol 6 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Semi-Supervised Machine Learning Approaches for DDOS Attack Detection

Share this