A Survey on the Applications of Machine Learning in Identifying Predominant Network Attacks

Heyshanthini Pandiyakumari S.¹ , Jaya R.²

1. Department of CSE, NHCE, VTU, Bangalore, India.
2. Department of CSE, NHCE, Bangalore, India.

Section:Survey Paper, Product Type: Journal-Paper
Vol.11 , Issue.5 , pp.16-22, Oct-2023

Online published on Oct 31, 2023

Copyright © Heyshanthini Pandiyakumari S., Jaya R. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Over the past decade, there has been an unprecedented surge in the number of intelligent devices, and in recent years, the proliferation of intelligent machines has surged significantly. Computer networks play a vital role in ensuring uninterrupted connectivity among interconnected IoT devices. The substantial increase in the use of smart devices has unfortunately paved the way for substantial unethical activities within networks. This study focuses on the predominant network threat known as the “Low Rate/Slow Denial of Service (LDoS) attack” which poses a substantial risk to the internet`s integrity. Detecting the source of these attacks is exceptionally challenging because they do not generate high volumes of traffic or sudden spikes in network activity. This survey explores the application of machine learning to enhance the detection of such attacks, aiming to achieve improved performance.

Key-Words / Index Term :
LDoS attack, DDoS attack, Anomaly detection, ML, RL, IDS, Hyperparameter optimization

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