首页|Enhancing Network Threat Detection with Random Forest-Based NIDS and Permutation Feature Importance

Enhancing Network Threat Detection with Random Forest-Based NIDS and Permutation Feature Importance

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  • NETL
  • NSTL
  • Springer Nature
Network Intrusion Detection Systems (NIDS) are critical for protecting computer networks from unauthorized activities. Traditional NIDS rely on rule-based signa-tures, which can be limiting in detecting emerging threats. This study investigates the effectiveness of the random forest classifier in advancing NIDS capabilities through machine learning. Using the CICIDS-2017 dataset, the data are preproc-essed to enhance their quality by removing redundancies. feature selection and permutation importance were employed to identify the most relevant features. The methodology involves rigorous testing and analysis of the random forest classifier's performance, focusing on fl-score rates compared to other machine learning mod-els. Results demonstrate that by optimizing class weights, applying a custom predic-tion function and leveraging 26 key features, the random forest classifier achieves an outstanding 99.8% in the weighted fl-score and 93.31% in the macro fl-score in various attack types. This research highlights the potential of machine learning to significantly enhance NIDS effectiveness, offering a robust defense mechanism against evolving cybersecurity threats in modern networks.

Network intrusion detection system (NIDS)Feature selectionPermutation importanceCICIDS-2017CyberSecurityRandom forestRule-basedSignatures

Mohammed Tarek Abdelaziz、Abdelrahman Radwan、Hesham Mamdouh、Adel Saeed Saad、Abdulrahman Salem Abuzaid、Ahmed Ayman AbdElhakeem、Salma Zakzouk、Kareem Moussa、M. Saeed Darweesh

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School of Engineering and Applied Sciences, Nile University, Giza 12677, Egypt

Faculty of Engineering, Helwan University, Giza, Egypt

Faculty of Engineering, Benha University, Giza, Egypt

School of Engineering and Applied Sciences, Nile University, Giza 12677, Egypt||Wireless Intelligent Networks Center (WINC), Nile University, Giza 12677, Egypt

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2025

10.1007/s10922-024-09874-0

Journal of network and systems management

Journal of network and systems management

SCI
ISSN：1064-7570
年,卷(期)：2025.33(1)
  • 66