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Machine learning based framework for network intrusion detection system using stacking ensemble technique

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Title Machine learning based framework for network intrusion detection system using stacking ensemble technique
 
Creator Parashar, Anshu
Saggu, Kuljot Singh
Garg, Anupam
 
Subject Neural Network
Cyber security
Intrusion detection system
Machine learning
 
Description 509-518
Cybersecurity issues are increasing day by day, and it is becoming essential to address them aggressively. An efficient
IDS system should be placed to identify abnormal behaviour by dynamically tracing the network traffic pattern. In this
work, we proposed a framework for Network Intrusion Detection System using stacking ensemble technique of machine
learning, which is testified on Random Forest Regressor and Extra Tree Classifier approaches for feature selections from the
subjected dataset. The extensive experimentation has been done by applying 11 states of the art and hybrid machine learning
algorithms to select the best performing algorithms. During the investigation, Random Forest, ID3 and XGBoost algorithms
are found as best performers among different machine learning algorithms based on accuracy, precision, recall, F1-score and
time to increase real-time attack detection performance. Three case studies have been carried out. Our results indicate that
the proposed stacking ensemble-based framework of NIDS outperformed compared to the different state of art machine
learning algorithms with average 0.99 prediction accuracy.
 
Date 2022-08-16T04:43:43Z
2022-08-16T04:43:43Z
2022-08
 
Type Article
 
Identifier 0975-1017 (Online); 0971-4588 (Print)
http://nopr.niscpr.res.in/handle/123456789/60279
https://doi.org/10.56042/ijems.v29i4.46838
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source IJEMS Vol.29(4) [AUG 2022]