Recognizing Network Threats Using Machine Learning: A Comprehensive Overview

• Author(s): Deen Mohd ; Mohd Vakil
• Paper ID: 1709884
• Page: 1310-1315
• Published Date: 31-07-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 1 July-2025

Abstract

The number of intelligent devices has increased at an unprecedented rate over the last ten years, and the spread of intelligent machines has increased dramatically in recent years. In order to guarantee constant communication amongst networked IoT devices, computer networks are essential. Unfortunately, the significant rise in the usage of smart devices has opened the door for significant unethical behavior within networks. The primary network danger under investigation in this study is the "Low Rate/Slow Denial of Service (LDoS) attack," which seriously jeopardizes the integrity of the internet. Due to the fact that these assaults do not produce large amounts of bandwidth or abrupt increases in network activity, identifying their source is quite difficult. This study investigates the use of machine learning to improve the detection.

Keywords

LDoS attack, DDoS attack, Anomaly detection, ML, RL, IDS, Hyper parameter optimization

Citations

IRE Journals:
Deen Mohd , Mohd Vakil "Recognizing Network Threats Using Machine Learning: A Comprehensive Overview" Iconic Research And Engineering Journals Volume 9 Issue 1 2025 Page 1310-1315

IEEE:
Deen Mohd , Mohd Vakil "Recognizing Network Threats Using Machine Learning: A Comprehensive Overview" Iconic Research And Engineering Journals, 9(1)