A Review of Machine Learning-based Intrusion Detection System

Authors

  • Nilamadhab Mishra Biju Patnaik University of Technology
  • Sarojananda Mishra Indira Gandhi Institute of Technology image/svg+xml

DOI:

https://doi.org/10.4108/eetiot.5332

Keywords:

Intrusion Detection, Machine Learning, Support Vector Machine, Dataset Attacks

Abstract

Intrusion detection systems are mainly prevalent proclivity within our culture today. Interference exposure systems function as countermeasures to identify web-based protection threats. This is a computer or software program that monitors unauthorized network activity and sends alerts to administrators. Intrusion detection systems scan for known threat signatures and anomalies in normal behaviour. This article also analyzed different types of infringement finding systems and modus operandi, focusing on support-vector-machines; Machine-learning; fuzzy-logic; and supervised-learning. For the KDD dataset, we compared different strategies based on their accuracy. Authors pointed out that using support vector machine and machine learning together improves accuracy. 

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Published

07-03-2024

How to Cite

[1]
N. Mishra and S. Mishra, “A Review of Machine Learning-based Intrusion Detection System”, EAI Endorsed Trans IoT, vol. 10, Mar. 2024.

Issue

Vol. 10 (2024): EAI Endorsed Transactions on Internet of Things

Section

Review article

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