Effective Feature Extraction for Intrusion Detection System using Non-negative Matrix Factorization and Univariate analysis

Authors: Swapnil Mane, Vaibhav Khatavkar, Niranjan Gijare, Pranav Bhendawade
Published: 2023-04-03

Source: https://arxiv.org/abs/2304.01166

PDF: https://arxiv.org/pdf/2304.01166

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機械学習手法 特徴選択手法 データ収集

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Abstract

An Intrusion detection system (IDS) is essential for avoiding malicious activity. Mostly, IDS will be improved by machine learning approaches, but the model efficiency is degrading because of more headers (or features) present in the packet (each record). The proposed model extracts practical features using Non-negative matrix factorization and chi-square analysis. The more number of features increases the exponential time and risk of overfitting the model. Using both techniques, the proposed model makes a hierarchical approach that will reduce the features quadratic error and noise. The proposed model is implemented on three publicly available datasets, which gives significant improvement. According to recent research, the proposed model has improved performance by 4.66