A comparative analysis of gradient boosting, random forest and deep neural networks in intrusion detection system
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Full Text |
Pdf
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Author |
Iftikhar Ahmad and Hadi S. Al Qahtani
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e-ISSN |
1819-6608 |
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On Pages
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1392-1402
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Volume No. |
18
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Issue No. |
12
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Issue Date |
August 30, 2023
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DOI |
https://doi.org/10.59018/0623177
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Keywords |
deep neural network, gradient boosting, IDS, machine learning, malware, random forest, security.
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Abstract
The growing threat of advanced security attacks targeting enterprise information systems raises the need for novel
security solutions that promptly identify and respond to these issues. These security strategies must automate threat
detection and response in enterprise settings, enabling organizations to address emerging threats, ongoing attacks, and
imminent risks adequately. Traditional security strategies that rely on rule-based approaches for intrusion detection systems
are inefficient in achieving these objectives due to their limited capabilities in identifying new threats. As a result, machine
learning strategies have been proposed to address these needs, offering an intelligent detection environment for novel
threats. Classification algorithms such as random forest, gradient boosting and deep learning techniques like deep neural
networks have been proposed in various studies. This paper examines the performance of these models, providing a
comparative review of their detection capabilities based on precision, recall, accuracy, specificity, and sensitivity. The
models are tested using a Python environment due to the extensive machine learning capabilities. These tests show that
random forest is the ideal model for network-based intrusion detection systems.
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