Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems

Qaiser Abbas; Sadaf Hina; Hamza Sajjad; Khurram Shabih Zaidi; Rehan Akbar

doi:10.7717/peerj-cs.1552

Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems

PeerJ Comput Sci. 2023 Sep 4:9:e1552. doi: 10.7717/peerj-cs.1552. eCollection 2023.

Authors

Qaiser Abbas¹, Sadaf Hina², Hamza Sajjad³, Khurram Shabih Zaidi⁴, Rehan Akbar⁵

Affiliations

¹ University of Engineering and Technology, Lahore, Pakistan.
² University of Salford, Salford, UK.
³ University of Engineering and Technology Lahore, Lahore, Pakistan.
⁴ COMSATS University Islamabad, Lahore, Pakistan.
⁵ Computer and Information Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.

Abstract

Network intrusion is one of the main threats to organizational networks and systems. Its timely detection is a profound challenge for the security of networks and systems. The situation is even more challenging for small and medium enterprises (SMEs) of developing countries where limited resources and investment in deploying foreign security controls and development of indigenous security solutions are big hurdles. A robust, yet cost-effective network intrusion detection system is required to secure traditional and Internet of Things (IoT) networks to confront such escalating security challenges in SMEs. In the present research, a novel hybrid ensemble model using random forest-recursive feature elimination (RF-RFE) method is proposed to increase the predictive performance of intrusion detection system (IDS). Compared to the deep learning paradigm, the proposed machine learning ensemble method could yield the state-of-the-art results with lower computational cost and less training time. The evaluation of the proposed ensemble machine leaning model shows 99%, 98.53% and 99.9% overall accuracy for NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets, respectively. The results show that the proposed ensemble method successfully optimizes the performance of intrusion detection systems. The outcome of the research is significant and contributes to the performance efficiency of intrusion detection systems and developing secure systems and applications.

Keywords: Ensemble method; Intrusion detection; Predictive modelling; Secure systems.

Grants and funding

This research work was supported by the Universiti Teknologi PETRONAS STIRF Research Grant Scheme Project (Cost Centre No. 015LA0-036). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.