Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems
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 a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-09-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1552.pdf |
| _version_ | 1827823205778194432 |
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| author | Qaiser Abbas Sadaf Hina Hamza Sajjad Khurram Shabih Zaidi Rehan Akbar |
| author_facet | Qaiser Abbas Sadaf Hina Hamza Sajjad Khurram Shabih Zaidi Rehan Akbar |
| author_sort | Qaiser Abbas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T02:08:50Z |
| format | Article |
| id | doaj.art-144a8e3cde0843f7ae681dfd20dbb249 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-12T02:08:50Z |
| publishDate | 2023-09-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-144a8e3cde0843f7ae681dfd20dbb2492023-09-06T15:05:10ZengPeerJ Inc.PeerJ Computer Science2376-59922023-09-019e155210.7717/peerj-cs.1552Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systemsQaiser Abbas0Sadaf Hina1Hamza Sajjad2Khurram Shabih Zaidi3Rehan Akbar4University of Engineering and Technology, Lahore, PakistanUniversity of Salford, Salford, UKUniversity of Engineering and Technology Lahore, Lahore, PakistanCOMSATS University Islamabad, Lahore, PakistanComputer and Information Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar, MalaysiaNetwork 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.https://peerj.com/articles/cs-1552.pdfPredictive modellingEnsemble methodIntrusion detectionSecure systems |
| spellingShingle | Qaiser Abbas Sadaf Hina Hamza Sajjad Khurram Shabih Zaidi Rehan Akbar Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems PeerJ Computer Science Predictive modelling Ensemble method Intrusion detection Secure systems |
| title | Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems |
| title_full | Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems |
| title_fullStr | Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems |
| title_full_unstemmed | Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems |
| title_short | Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems |
| title_sort | optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems |
| topic | Predictive modelling Ensemble method Intrusion detection Secure systems |
| url | https://peerj.com/articles/cs-1552.pdf |
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