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توسعه یک سیستم تشخیص نفوذ مبتنی بر خوشهبندی فازی و الگوریتم بهینهسازی نهنگ | ||
علوم و فناوریهای پدافند نوین | ||
دوره 12، شماره 2 - شماره پیاپی 44، مرداد 1400، صفحه 143-158 اصل مقاله (661.28 K) | ||
نوع مقاله: مقاله پژوهشی | ||
نویسندگان | ||
رضا نظری1؛ مصطفی فخراحمد* 2 | ||
1دانشکده علوم و فنون نوین، دانشگاه تهران، تهران، ایران | ||
2بخش مهندسی و علوم کامپیوتر و فناوری اطلاعات، دانشکده برق و کامپیوتر، دانشگاه شیراز، شیراز، ایران. | ||
تاریخ دریافت: 31 تیر 1398، تاریخ بازنگری: 30 دی 1399، تاریخ پذیرش: 02 بهمن 1399 | ||
چکیده | ||
امروزه شبکههای کامپیوتری در جهان کاربردهای فراوانی پیدا کردهاند. بهدلیل استفاده گسترده از اینترنت، سیستمهای رایانهای، مستعد سرقت اطلاعات هستند که منجر به ظهور سیستمهای تشخیص نفوذ (IDS) شده است. امنیت شبکه در پاسخ به افزایش اطلاعات حساس، به یک موضوع اساسی در علوم کامپیوتر تبدیل شده است. در پژوهش حاضر سیستم تشخیص نفوذ غیرنظارتی مبتنی بر خوشهبندی فازی (FCM) با بهرهگیری از الگوریتم بهینهسازی نهنگ (WOA) پیشنهاد شده است و با مجموعه داده استاندارد تشخیص نفوذ 99 KDD Cup مورد آزمایش قرار گرفت. در این روش بهمنظور جداسازی فعالیتهای نفوذی از فعالیتهای عادی، ﺧﻮﺷﻪﺑﻨﺪﯼ C- میانگین فازی مورد استفاده قرارگرفته و از الگوریتم بهینهسازی نهنگ برای بهدست آوردن تفکیک بهینه بین این فعالیتها استفاده شده است. جهت کمک به FCM، از WOA استفاده شده است تا از مراکز خوشههای اولیه مناسب بهجای مراکز تصادفی استفاده کند. نتایج تجربی بر روی مجموعه داده 99KDD Cup حاکی از بهبود نرخ همگرایی، صحت و همچنین نرخ هشدار اشتباه توسط الگوریتم WOA-FCM در قیاس با سایر روشهای غیر نظارتی میباشد. از همینرو، یافتههای پژوهش حاضر میتواند در زمینه حل مسائل پیچیده مرتبط با IDS مؤثر واقع شود. | ||
کلیدواژهها | ||
سیستم تشخیص نفوذ (IDS)؛ خوشهبندی C- میانگین فازی (FCM)؛ الگوریتم بهینهسازی نهنگ (WOA)؛ منطق فازی؛ خوشهبندی فازی؛ WOA-FCM | ||
عنوان مقاله [English] | ||
Developing an Intrusion Detection System Based on Fuzzy Clustering and Whale Optimization Algorithm | ||
نویسندگان [English] | ||
Reza Nazari1؛ Mostafa Fakhrahmad2 | ||
1Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran | ||
2Dept. of Computer Science & Engineering & IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran | ||
چکیده [English] | ||
Nowadays, computer networks are being widely used in the world. Due to the widespread use of the internet, computer systems are prone to information theft and this has led to the emergence of intrusion detection systems (IDS). Thus, network security has become an essential subject in computer science responding to the increase of sensitive information. The current research has used fuzzy C-means (FCM) and Whale optimization algorithm (WOA) to propose an unsupervised machine learning intrusion identification system and has tested it with the KDD Cup 99 standard intrusion detection dataset. In this method, fuzzy C-means has been applied in order to distinguish intrusive activities from normal activities and Whale optimization algorithm has been used to achieve optimal separations among these activities. In order to help FCM, the WOA has been applied to start with suitable cluster centers rather than randomly initialized centers. Experimental results on KDD Cup 99 dataset showed that the proposed method offers higher detection accuracy and a lower false alarm rate compared to other similar algorithms. Therefore, the findings of the present study would be effective in solving complex problems related to IDS. | ||
کلیدواژهها [English] | ||
Intrusion Detection System (IDS), Fuzzy C-Means (FCM), Whale Optimization Algorithm (WOA), Fuzzy Logic, Fuzzy Clustering, WOA-FCM | ||
مراجع | ||
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