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Network Intrusion Classification Using Machine Learning and Clustered Features
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DOI：10.1049/tje2.70169.png)
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Abstract
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As network-based services continue to expand and cyber threats grow increasingly sophisticated, the deployment of effective network intrusion detection systems (NIDS) has become crucial for the protection of sensitive data and the preservation of digital infrastructure integrity. These systems are indispensable in ensuring the security and reliability of digital environments. This study presents an all-encompassing approach that integrates feature selection, ensemble learning, clustering, and diverse analytical methods to improve the precision and effectiveness of network intrusion detection systems. Using data sets that have been preprocessed and classified using the Random Forest and XGBoost algorithms, we extract key features and categorize key variables using K-means clustering, thus constructing a robust feature set for intrusion detection. Our experimental results demonstrate high precision in detecting benign and malicious activities using the CIC-IDS-17 and UNSW-NB15 datasets. These outcomes underscore the efficacy of our proposed approach in monitoring network traffic, effectively identifying threats, and preventing potential intrusions.
Keywords:
network intrusion detection
XGBoost
K-means clustering
feature fusion
machine learning
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