Hybrid Ensemble of RF-DNN Model for BENIGN and Attack Traffic Classification in Intrusion System

DOI

10.2991/978-94-6239-664-7_65How to use a DOI?

Keywords

Network Intrusion Detection; BENIGN Traffic Classification; Hybrid Ensemble Model; Soft Voting; Cyber Threats

Abstract

In recent years, the rapid growth of cyber threats has emphasized the importance of accurate network intrusion detection systems (NIDS). While many machine learning and deep learning models have shown promise in identifying various types of malicious traffic with the accurate classification of BENIGN traffic remains a challenge due to high false positive rates. In this paper, I propose a hybrid ensemble model that combines a Random Forest (RF) classifier with a Deep Neural Network (DNN) using a soft voting strategy to improve the detection of BENIGN network traffic. The RF and DNN classifiers independently predict class probabilities, which are then averaged to form the final decision in the hybrid ensemble. Experimental results demonstrate that the proposed hybrid model outperforms the standalone RF and DNN models by achieving higher accuracy of false positive rate for the BENIGN class. These findings highlight the potential of ensemble learning techniques to enhance the reliability of intrusion detection systems by improving normal traffic classification.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Farhan Tanvir Ahmed
AU  - Riaz Mahmood
PY  - 2026
DA  - 2026/06/08
TI  - Hybrid Ensemble of RF-DNN Model for BENIGN and Attack Traffic Classification in Intrusion System
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 947
EP  - 962
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_65
DO  - 10.2991/978-94-6239-664-7_65
ID  - Ahmed2026
ER  -