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An intrusion detection system with a parallel multi-layer neural network | ||
| Journal of Mathematical Modeling | ||
| مقاله 8، دوره 9، شماره 3، آذر 2021، صفحه 437-450 اصل مقاله (377.35 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2021.17362.1502 | ||
| نویسندگان | ||
| Mohammad Hassan Nataj Solhdar1؛ Mehdi Janinasab Solahdar2؛ Sadegh Eskandari* 3 | ||
| 1Shohadaye Hoveizeh University of Technology, Dasht-e Azadegan, Khuzestan, Iran | ||
| 2Islamic Azad University, Mahalat Branch, Mahalat, Iran | ||
| 3Department of Computer Science, University of Guilan, Rasht, Iran | ||
| چکیده | ||
| Intrusion detection is a very important task that is responsible for supervising and analyzing the incidents that occur in computer networks. We present a new anomaly-based intrusion detection system (IDS) that adopts parallel classifiers using RBF and MLP neural networks. This IDS constitutes different analyzers each responsible for identifying a certain class of intrusions. Each analyzer is trained independently with a small category of related features. The proposed IDS is compared extensively with existing state-of-the-art methods in terms of classification accuracy . Experimental results demonstrate that our IDS achieves a true positive rate (TPR) of 98.60\% on the well-known NSL-KDD dataset and therefore this method can be considered as a new state-of-the-art anomaly-based IDS. | ||
| کلیدواژه‌ها | ||
| Intrusion detection؛ computer security؛ neural network؛ parallel processing | ||
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آمار تعداد مشاهده مقاله: 805 تعداد دریافت فایل اصل مقاله: 801 |
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