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IoT 24(1):

Research Article

A Review of Machine Learning-based Intrusion Detection System

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  • @ARTICLE{10.4108/eetiot.5332,
        author={Nilamadhab Mishra and Sarojananda Mishra},
        title={A Review of Machine Learning-based Intrusion Detection System},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={IOT},
        year={2024},
        month={3},
        keywords={Intrusion Detection, Machine Learning, Support Vector Machine, Dataset Attacks},
        doi={10.4108/eetiot.5332}
    }
    
  • Nilamadhab Mishra
    Sarojananda Mishra
    Year: 2024
    A Review of Machine Learning-based Intrusion Detection System
    IOT
    EAI
    DOI: 10.4108/eetiot.5332
Nilamadhab Mishra1,*, Sarojananda Mishra2
  • 1: Biju Patnaik University of Technology
  • 2: Indira Gandhi Institute of Technology
*Contact email: nilamadhab76@gmail.com

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. 

Keywords
Intrusion Detection, Machine Learning, Support Vector Machine, Dataset Attacks
Received
2023-12-11
Accepted
2024-03-01
Published
2024-03-07
Publisher
EAI
http://dx.doi.org/10.4108/eetiot.5332

Copyright © 2024 N. Mishra et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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