Improving Network Security: An Intelligent IDS with RNN-LSTM and Grey Wolf Optimization
While this dependence on interconnected computer networks and the web requires robust cybersecurity. Cyber threats have been met with solutions like Intrusion Detection Systems (IDS). IDS are commonly rule-based, and very often use either signature-based or heuristic approaches to detect intrusions...
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College of Computer and Information Technology – University of Wasit, Iraq
2024-12-01
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| Series: | Wasit Journal of Computer and Mathematics Science |
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| Online Access: | http://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/264 |
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| author | murtadha ali |
| author_facet | murtadha ali |
| author_sort | murtadha ali |
| collection | DOAJ |
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While this dependence on interconnected computer networks and the web requires robust cybersecurity. Cyber threats have been met with solutions like Intrusion Detection Systems (IDS). IDS are commonly rule-based, and very often use either signature-based or heuristic approaches to detect intrusions. Therefore, for such we recommend an IDS that merges the Grey Wolf Optimization (GWO) algorithm and Recurrent Neural Networks with Long Short-Term Memory (RNN-LSTM). RNN-LSTM to Handle Dynamic Network data, but not provided enough complain details in model training. Based on the behavior of grey wolf, an optimization technique GWO is implemented for intrusion detection to enhance accuracy and minimize false alarm in RNN-LSTM. Preprocess and segment network data with creating RNN-LSTM model for considering the dependence of our dataset Our approach improves the IDS performance by optimizing hyperparameters such as hidden layers, units, learning rates using GWO. The architecture of this RNN-LSTM with GWO IDS provides capable and responsive intrusion detection, training on previous data to be able to detect new threats. Made for network security by combining deep learning and optimization, tests reached 99.5% accurate.
The research advances IDSs, addressing the limitations of traditional systems, and underscores the potential of AI and optimization in complex network security. This study demonstrates the promise of RNN-LSTM and GWO for creating robust, adaptive intrusion detection systems in intricate network environments.
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| format | Article |
| id | doaj-art-043ef78a006e4c418365d859f59e16ef |
| institution | Kabale University |
| issn | 2788-5879 2788-5887 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | College of Computer and Information Technology – University of Wasit, Iraq |
| record_format | Article |
| series | Wasit Journal of Computer and Mathematics Science |
| spelling | doaj-art-043ef78a006e4c418365d859f59e16ef2025-01-30T05:23:43ZengCollege of Computer and Information Technology – University of Wasit, IraqWasit Journal of Computer and Mathematics Science2788-58792788-58872024-12-013410.31185/wjcms.264Improving Network Security: An Intelligent IDS with RNN-LSTM and Grey Wolf Optimizationmurtadha ali0Iraqi Ministry of Education, While this dependence on interconnected computer networks and the web requires robust cybersecurity. Cyber threats have been met with solutions like Intrusion Detection Systems (IDS). IDS are commonly rule-based, and very often use either signature-based or heuristic approaches to detect intrusions. Therefore, for such we recommend an IDS that merges the Grey Wolf Optimization (GWO) algorithm and Recurrent Neural Networks with Long Short-Term Memory (RNN-LSTM). RNN-LSTM to Handle Dynamic Network data, but not provided enough complain details in model training. Based on the behavior of grey wolf, an optimization technique GWO is implemented for intrusion detection to enhance accuracy and minimize false alarm in RNN-LSTM. Preprocess and segment network data with creating RNN-LSTM model for considering the dependence of our dataset Our approach improves the IDS performance by optimizing hyperparameters such as hidden layers, units, learning rates using GWO. The architecture of this RNN-LSTM with GWO IDS provides capable and responsive intrusion detection, training on previous data to be able to detect new threats. Made for network security by combining deep learning and optimization, tests reached 99.5% accurate. The research advances IDSs, addressing the limitations of traditional systems, and underscores the potential of AI and optimization in complex network security. This study demonstrates the promise of RNN-LSTM and GWO for creating robust, adaptive intrusion detection systems in intricate network environments. http://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/264Cybersecurity measuresIntrusion Detection Systems (IDS)Grey Wolf Optimization (GWO) algorithm(RNN-LSTM)Optimization |
| spellingShingle | murtadha ali Improving Network Security: An Intelligent IDS with RNN-LSTM and Grey Wolf Optimization Wasit Journal of Computer and Mathematics Science Cybersecurity measures Intrusion Detection Systems (IDS) Grey Wolf Optimization (GWO) algorithm (RNN-LSTM) Optimization |
| title | Improving Network Security: An Intelligent IDS with RNN-LSTM and Grey Wolf Optimization |
| title_full | Improving Network Security: An Intelligent IDS with RNN-LSTM and Grey Wolf Optimization |
| title_fullStr | Improving Network Security: An Intelligent IDS with RNN-LSTM and Grey Wolf Optimization |
| title_full_unstemmed | Improving Network Security: An Intelligent IDS with RNN-LSTM and Grey Wolf Optimization |
| title_short | Improving Network Security: An Intelligent IDS with RNN-LSTM and Grey Wolf Optimization |
| title_sort | improving network security an intelligent ids with rnn lstm and grey wolf optimization |
| topic | Cybersecurity measures Intrusion Detection Systems (IDS) Grey Wolf Optimization (GWO) algorithm (RNN-LSTM) Optimization |
| url | http://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/264 |
| work_keys_str_mv | AT murtadhaali improvingnetworksecurityanintelligentidswithrnnlstmandgreywolfoptimization |