Tier-Based Optimization for Synthesized Network Intrusion Detection System
The innovation and evolution of hacking methodologies have led to a sharp rise in cyber attacks, highlighting the need for enhanced network security approaches. Network intrusion detection systems based on machine learning are playing a significant role in the domain of network security. However, de...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9916253/ |
| _version_ | 1811182586002669568 |
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| author | Murtaza Ahmed Siddiqi Wooguil Pak |
| author_facet | Murtaza Ahmed Siddiqi Wooguil Pak |
| author_sort | Murtaza Ahmed Siddiqi |
| collection | DOAJ |
| description | The innovation and evolution of hacking methodologies have led to a sharp rise in cyber attacks, highlighting the need for enhanced network security approaches. Network intrusion detection systems based on machine learning are playing a significant role in the domain of network security. However, designing an optimal framework for a network intrusion detection system is an ongoing concern. In this study, an optimal framework for a network intrusion detection system based on image processing is proposed. The framework is a fusion of augmented feature selection flow with an image transformation and enhancement methodology. Initially, the proposed framework reduces the number of features to achieve overall efficiency. Later, the non-image data is transformed into images. The transformed images are then enhanced for achieving effective anomaly detection based on a deep-learning classifier. The proposed method is implemented on three diverse benchmark datasets of intrusion detection. To illustrate the efficiency of the proposed framework it is compared with some of the most recent publications on image-processing-based network intrusion detection systems. |
| first_indexed | 2024-04-11T09:34:18Z |
| format | Article |
| id | doaj.art-41a4edcedfdc42099d090b7c11aec1aa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T09:34:18Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-41a4edcedfdc42099d090b7c11aec1aa2022-12-22T04:31:46ZengIEEEIEEE Access2169-35362022-01-011010853010854410.1109/ACCESS.2022.32139379916253Tier-Based Optimization for Synthesized Network Intrusion Detection SystemMurtaza Ahmed Siddiqi0Wooguil Pak1https://orcid.org/0000-0002-9551-7373Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaThe innovation and evolution of hacking methodologies have led to a sharp rise in cyber attacks, highlighting the need for enhanced network security approaches. Network intrusion detection systems based on machine learning are playing a significant role in the domain of network security. However, designing an optimal framework for a network intrusion detection system is an ongoing concern. In this study, an optimal framework for a network intrusion detection system based on image processing is proposed. The framework is a fusion of augmented feature selection flow with an image transformation and enhancement methodology. Initially, the proposed framework reduces the number of features to achieve overall efficiency. Later, the non-image data is transformed into images. The transformed images are then enhanced for achieving effective anomaly detection based on a deep-learning classifier. The proposed method is implemented on three diverse benchmark datasets of intrusion detection. To illustrate the efficiency of the proposed framework it is compared with some of the most recent publications on image-processing-based network intrusion detection systems.https://ieeexplore.ieee.org/document/9916253/CNNCSE-CIC-IDS 2018CIC-IDS 2017ISCX-IDS 2012intrusion detectionnetwork intrusion detection system |
| spellingShingle | Murtaza Ahmed Siddiqi Wooguil Pak Tier-Based Optimization for Synthesized Network Intrusion Detection System IEEE Access CNN CSE-CIC-IDS 2018 CIC-IDS 2017 ISCX-IDS 2012 intrusion detection network intrusion detection system |
| title | Tier-Based Optimization for Synthesized Network Intrusion Detection System |
| title_full | Tier-Based Optimization for Synthesized Network Intrusion Detection System |
| title_fullStr | Tier-Based Optimization for Synthesized Network Intrusion Detection System |
| title_full_unstemmed | Tier-Based Optimization for Synthesized Network Intrusion Detection System |
| title_short | Tier-Based Optimization for Synthesized Network Intrusion Detection System |
| title_sort | tier based optimization for synthesized network intrusion detection system |
| topic | CNN CSE-CIC-IDS 2018 CIC-IDS 2017 ISCX-IDS 2012 intrusion detection network intrusion detection system |
| url | https://ieeexplore.ieee.org/document/9916253/ |
| work_keys_str_mv | AT murtazaahmedsiddiqi tierbasedoptimizationforsynthesizednetworkintrusiondetectionsystem AT wooguilpak tierbasedoptimizationforsynthesizednetworkintrusiondetectionsystem |