DDoS Detection on IoT Devices Using Machine Learning Techniques

Enisha J; Mrs. K. Gowri; Harini.P; Praveen Kumar.M

doi:10.71097/IJSAT.v16.i1.2360

DDoS Detection on IoT Devices Using Machine Learning Techniques

Author(s)	Enisha J, Mrs. K. Gowri, Harini.P, Praveen Kumar.M
Country	India
Abstract	With the emergence of technology, the usage of IoT (Internet of Things) devices is said to be increasing in people’s lives. Such devices can benefit the average individual, who does not necessarily have to have technical knowledge. The IoT can be found in home security and alarm systems, smart fridges, smart televisions, and more. Although small Internet-connected devices have numerous benefits and can help enhance people’s efficiency, they also can pose a security threat. Malicious actors often attempt to find new ways to exploit and utilize certain resources, and IoT devices are a perfect candidate for such exploitation due to the huge volume of active devices. This is particularly true for Distributed Denial of Service (DDoS) attacks, which involve the exploitation of a massive number of devices, such as IoT devices, to act as bots and send fraudulent requests to services, thus obstructing them. To identify and detect whether such attacks have occurred or not in a network, there must be a reliable mechanism of detection based on adequate techniques. The most common technique for this purpose is artificial intelligence, which involves the use of Machine Learning (ML) and Deep Learning (DL) to help identify cyberattacks. ML models involve algorithms that use structured data to learn from, predict outcomes from, and identify patterns. The goal of this paper is to review selected studies and publications relevant to the topic of DDoS detection in IoT-based networks using machine-learning-relevant publications. It offers a wealth of references for academics looking to define or expand the scope of their research in this area.
Keywords	DDoS Attack, Machine Learning, ANN, XG Boost, AdaBoost, SVM, Random Forest.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-03-13
Cite This	DDoS Detection on IoT Devices Using Machine Learning Techniques - Enisha J, Mrs. K. Gowri, Harini.P, Praveen Kumar.M - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2360
DOI	https://doi.org/10.71097/IJSAT.v16.i1.2360
Short DOI	https://doi.org/g88scb

View / Download PDF File

doi

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

IJSAT DOI prefix is
10.71097/IJSAT

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 4 Cover Page Vol 15 Isu 3 Cover Page Vol 15 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 IJSAT 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	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology

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

DDoS Detection on IoT Devices Using Machine Learning Techniques

Share this