Enhancing Intrusion Detection Systems Using Metaheuristic Algorithms
In the current network security framework, Intrusion Detection Systems (IDSs) happen to be among the major players in ensuring that the network activity is being monitored round the clock for any intrusions which may occur. The rising degree of cyber threats’ intricacy enforces the constant developm...
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| Veröffentlicht in: | DIYALA JOURNAL OF ENGINEERING SCIENCES Jg. 17; H. 3; S. 15 - 31 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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University of Diyala
01.09.2024
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| ISSN: | 1999-8716, 2616-6909 |
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| Abstract | In the current network security framework, Intrusion Detection Systems (IDSs) happen to be among the major players in ensuring that the network activity is being monitored round the clock for any intrusions which may occur. The rising degree of cyber threats’ intricacy enforces the constant development of IDS methodologies to maintain effectiveness in detecting and reversing the emergence of any extra risks. Therefore, to settle the matter featured by, this research studies try to incorporate the most powerful metaheuristic algorithms, Lion Optimization Algorithm (LOA) and Grey Wolf Optimizer (GWO) in particular, to develop better detection accuracy and efficiency. The core obstacle recognized in this article is the fact that many systems of IDS send out false alarms and their mechanisms of detection of the true anomalies need to be improved immensely. In a nutshell, the change would unveil a fresh way of using LOA and GWO using them to promote the enhancement of internet defences systems in real-time. These schemes can discover previously unknown weaknesses or stealthy attacks. The core of this undertaking would consist in the conception and implementing of a Hybrid Network Intrusion Detection System, which will be created by blending the Lion Optimization Feature Selection (LOFS) and GWO smelters, denoted as LOFSGWO. Critically, the main purpose is to incorporate the GWO as a tool in the operations to cut down the dangerous parameters favourable towards an intrusion mechanism in the framework of a Hybrid CNN-LSTM Deep Learning system. Model tests reveal over 99.26% accuracy of low negative samples into out of a box that are served as testing as well as NSL-KDD dataset, which are similar to the simulation of WUSTL-EOM 2020 system. The obtained outcomes verify the relevance and efficiency of the suggested strategy, which may be used in the resolution of the issues faced in a network security today. |
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| AbstractList | In the current network security framework, Intrusion Detection Systems (IDSs) happen to be among the major players in ensuring that the network activity is being monitored round the clock for any intrusions which may occur. The rising degree of cyber threats’ intricacy enforces the constant development of IDS methodologies to maintain effectiveness in detecting and reversing the emergence of any extra risks. Therefore, to settle the matter featured by, this research studies try to incorporate the most powerful metaheuristic algorithms, Lion Optimization Algorithm (LOA) and Grey Wolf Optimizer (GWO) in particular, to develop better detection accuracy and efficiency. The core obstacle recognized in this article is the fact that many systems of IDS send out false alarms and their mechanisms of detection of the true anomalies need to be improved immensely. In a nutshell, the change would unveil a fresh way of using LOA and GWO using them to promote the enhancement of internet defences systems in real-time. These schemes can discover previously unknown weaknesses or stealthy attacks. The core of this undertaking would consist in the conception and implementing of a Hybrid Network Intrusion Detection System, which will be created by blending the Lion Optimization Feature Selection (LOFS) and GWO smelters, denoted as LOFSGWO. Critically, the main purpose is to incorporate the GWO as a tool in the operations to cut down the dangerous parameters favourable towards an intrusion mechanism in the framework of a Hybrid CNN-LSTM Deep Learning system. Model tests reveal over 99.26% accuracy of low negative samples into out of a box that are served as testing as well as NSL-KDD dataset, which are similar to the simulation of WUSTL-EOM 2020 system. The obtained outcomes verify the relevance and efficiency of the suggested strategy, which may be used in the resolution of the issues faced in a network security today. |
| Author | Mohammed Fadhil, Heba Dawood, Zinah Osamah Al Mhdawi, Ammar |
| Author_xml | – sequence: 1 givenname: Heba surname: Mohammed Fadhil fullname: Mohammed Fadhil, Heba – sequence: 2 givenname: Zinah Osamah surname: Dawood fullname: Dawood, Zinah Osamah – sequence: 3 givenname: Ammar surname: Al Mhdawi fullname: Al Mhdawi, Ammar |
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| Cites_doi | 10.1016/j.comnet.2023.109662 10.3390/electronics11182885 10.1145/382912.382923 10.1016/j.jcde.2015.06.003 10.5121/ijnsa.2021.13102 10.1186/s40537-024-00887-9 10.1109/INFOCOMWKSHPS57453.2023.10225791 10.1201/9781003406105-14 10.12720/jait.13.1.36-44 10.1016/j.compeleceng.2023.108626 10.1109/ACCESS.2021.3128837 10.1016/j.jisa.2022.103405 10.3390/math11081887 10.1016/j.seta.2022.102311 10.3390/jcp1010011 10.3390/sym13101814 10.1007/s00500-023-08678-9 10.1016/j.iot.2023.100887 10.1007/s11227-022-04568-3 10.54216/JCIM.090106 10.1109/OTCON56053.2023.10113978 10.1109/ACCESS.2020.3000421 10.32604/csse.2023.041446 10.1007/s11042-023-14795-2 10.1016/j.inffus.2022.09.026 10.1016/j.knosys.2021.107894 10.1016/j.advengsoft.2013.12.007 10.1007/s40860-021-00169-8 10.1109/CISDA.2009.5356528 10.1007/s10207-022-00616-4 10.3991/ijim.v16i14.30197 |
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| SubjectTerms | Intrusion Detection System; metaheuristic algorithms; Lion Optimization Algorithm ; Grey Wolf Optimization; Hyperparameter; Feature Selection; Deep Learning |
| Title | Enhancing Intrusion Detection Systems Using Metaheuristic Algorithms |
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