Article: Enhancing intrusion detection: combining LogitBoost algorithms and random forest Journal: International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC) 2025 Vol.49 No.2 pp.119 - 128 Abstract: Network data security is an issue that affects individuals, businesses, and governments worldwide. As attacks become more common and attackers' tactics evolve, it is important to implement advanced network security solutions such as an intrusion detection system (IDS) to detect unwanted and unexpected network activity. To that end, this article proposes a comprehensive strategy for improving detection performance through classification approaches. If only one classifier is utilised, the final decision may be erroneous, as incorrect classifier output may occur. The ensemble classification method combines multiple classifiers and produces better results than a single classifier. To improve classification accuracy, the proposed model incorporates random forest and LogitBoost. The proposed model has an accuracy of 95.89%, 99.91%, and 98.54% on the UNSW-NB15, CICIDS2017, and CICIDS2018 datasets, respectively, and outperforms other existing models in terms of accuracy, detection rate, and false alarm rate. Inderscience Publishers - linking academia, business and industry through research

Title: Enhancing intrusion detection: combining LogitBoost algorithms and random forest

Authors: Ankit Kharwar; Diya Vadhwani; Dipak Dabhi; Vivaksha Jariwala

Addresses: Information Technology, Sarvajanik College of Engineering and Technology, Sarvajanik University, Surat, Gujarat, India ' Computer Science and Engineering, Adani Institute of Infrastructure Engineering, Adani University, Ahmedabad, Gujarat, India ' Kaushalya The Skill University, Ahmedabad, Gujarat, India ' Information Technology, Sarvajanik College of Engineering and Technology, Sarvajanik University, Surat, Gujarat, India

Abstract: Network data security is an issue that affects individuals, businesses, and governments worldwide. As attacks become more common and attackers' tactics evolve, it is important to implement advanced network security solutions such as an intrusion detection system (IDS) to detect unwanted and unexpected network activity. To that end, this article proposes a comprehensive strategy for improving detection performance through classification approaches. If only one classifier is utilised, the final decision may be erroneous, as incorrect classifier output may occur. The ensemble classification method combines multiple classifiers and produces better results than a single classifier. To improve classification accuracy, the proposed model incorporates random forest and LogitBoost. The proposed model has an accuracy of 95.89%, 99.91%, and 98.54% on the UNSW-NB15, CICIDS2017, and CICIDS2018 datasets, respectively, and outperforms other existing models in terms of accuracy, detection rate, and false alarm rate.

Keywords: LogitBoost algorithm; network security; anomaly detection; machine learning; intrusion detection; random forest; boosting algorithm; ensemble methods.

DOI: 10.1504/IJAHUC.2025.146425

International Journal of Ad Hoc and Ubiquitous Computing, 2025 Vol.49 No.2, pp.119 - 128

Received: 11 Jul 2024
Accepted: 18 Sep 2024
Published online: 29 May 2025 *

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Title: Enhancing intrusion detection: combining LogitBoost algorithms and random forest

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