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Application of fixed structure learning automata for designing intrusion detection systems | ||
| Journal of Mathematical Modeling | ||
| مقاله 6، دوره 13، شماره 4، اسفند 2025، صفحه 831-850 اصل مقاله (760.24 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2025.29713.2656 | ||
| نویسنده | ||
| Kayvan Asghari* | ||
| Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran | ||
| چکیده | ||
| Designing an efficient intrusion detection system involves several phases, with feature selection being one of the most important. In this paper, a fixed-structure learning automata has been applied for the feature selection phase. The introduced method includes the exploration and exploitation phases of an optimization method to find the significant features of the network events to detect intrusions. The count of the selected features in the proposed method is a pre-defined number, as the feature selection is a multi-objective problem, and one of its objectives is the feature count. The learning automata-based method uses reward and penalty operators to explore the problem's search space. The proposed method enhances the intrusion detection accuracy rate, another significant objective for a feature selection method. Two well-known intrusion detection datasets called NSL-KDD and UNSW-NB15 have been used in this paper to evaluate the proposed method. The evaluation results indicate the acceptable performance of the proposed method compared with some of the existing ones. | ||
| کلیدواژه‌ها | ||
| Fixed structure learning automata؛ feature selection problem؛ intrusion detection system | ||
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آمار تعداد مشاهده مقاله: 50 تعداد دریافت فایل اصل مقاله: 37 |
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