Research Article
A Review of Machine Learning-based Intrusion Detection System
@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
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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