The Study of Feature Engineering in Machine Learning and Deep Learning for Network Intrusion Detection Systems
Publication Date
1-1-2024
Document Type
Conference Proceeding
Publication Title
2024 Silicon Valley Cybersecurity Conference, SVCC 2024
DOI
10.1109/SVCC61185.2024.10637359
Abstract
With the rise of sophisticated cyberattacks, the efficiency of intrusion detection systems becomes paramount. Machine learning (ML) and deep learning (DL) models used for intrusion detection often encounter datasets with irrelevant or redundant features, leading to low performance. To address this challenge, feature engineering techniques are important in extracting the most informative features, enabling faster and more accurate detection of malicious patterns. This paper investigates a comparative analysis of four feature engineering methods using a historical archival dataset: entropy, mutual information, chi- squared statistics, and ANOVA. By evaluating and comparing the effectiveness of these methods under different conditions of ML/DL models, this study aims to provide insights into their respective strengths and weaknesses, guiding the selection of the most suitable approach to improve the efficiency of network intrusion detection systems.
Funding Number
2244597
Funding Sponsor
National Science Foundation
Keywords
Deep Learning Models, Feature Engineering, Machine Learning Models, Network Intrusion Detection System, NSL-KDD Datasets
Department
Computer Engineering
Recommended Citation
Steven Ning, Khanh Nguyen, Sohini Bagchi, and Younghee Park. "The Study of Feature Engineering in Machine Learning and Deep Learning for Network Intrusion Detection Systems" 2024 Silicon Valley Cybersecurity Conference, SVCC 2024 (2024). https://doi.org/10.1109/SVCC61185.2024.10637359
