ارائه راهکاری نوین برای طبقه‌بندی ترافیک رمزنگاری‌شده با بهره‌گیری از یادگیری عمیق | ||
| پژوهش های نظری و کاربردی هوش ماشینی | ||
| مقاله 2، دوره 2، شماره 2، اسفند 1403، صفحه 17-29 اصل مقاله (835 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/abmir.2025.22525.1084 | ||
| نویسندگان | ||
| پویا ربیعی دولت آبادی1؛ مصطفی بستام* 2؛ خدیجه آقاجانی2 | ||
| 1کارشناسی ارشد گروه مهندسی کامپیوتر، دانشکده فناوری و مهندسی، دانشگاه مازندران، بابلسر، ایران | ||
| 2استادیار گروه مهندسی کامپیوتر، دانشکده فناوری و مهندسی، دانشگاه مازندران، بابلسر، ایران | ||
| چکیده | ||
| تحلیل ترافیک شبکه یکی از ارکان اساسی در بهبود امنیت و مدیریت کارآمد شبکه‌های کامپیوتری است. با توجه به گسترش روزافزون شبکه‌های کامپیوتری و پیچیدگی‌های ترافیک موجود در آن‌ها، شناسایی دقیق و سریع انواع ترافیک ازجمله ترافیک رمزنگاری‌شده، از اهمیت ویژه‌ای برخوردار است. در این راستا، استفاده از تکنیک‌های یادگیری ماشین می‌تواند ابزار قدرتمندی برای تحلیل و شناسایی دقیق الگوهای ترافیکی باشد. این مقاله به بررسی روش‌های پیشرفته شناسایی ترافیک در شبکه‌های کامپیوتری با بهره‌گیری از تکنیک‌های یادگیری ماشین پرداخته است. هدف اصلی این تحقیق، توسعه مدلی کارآمد و دقیق برای شناسایی و طبقه‌بندی انواع مختلف ترافیک شبکه، به‌ویژه ترافیک رمزنگاری‌شده، است. در این راستا، از مدل یادگیری عمیق VGG16 استفاده‌شده است. این مدل به دلیل ساختار لایه‌ای عمیق و توانایی تحلیل داده‌های حجیم، عملکرد برجسته‌ای در شناسایی الگوهای پیچیده ترافیک شبکه ارائه داده است. VGG16 قادر است با دقت بالا، انواع مختلف ترافیک شبکه را شناسایی و طبقه‌بندی کند، که این امر منجر به بهبود مدیریت ترافیک در شبکه می‌شود. در سناریوهای بررسی‌شده در این تحقیق، این مدل توانست دقت 99 درصدی را در شناسایی ترافیک رمزنگاری‌شده به‌دست آورد. | ||
| کلیدواژه‌ها | ||
| یادگیری عمیق؛ تحلیل ترافیک شبکه؛ ترافیک رمزشده؛ مدل VGG16؛ مدیریت ترافیک شبکه | ||
| عنوان مقاله [English] | ||
| A Novel Approach for Encrypted Traffic Classification Using Deep Learning | ||
| نویسندگان [English] | ||
| Pooya Rabiei Dolatabadi1؛ Mostafa Bastam2؛ Khadijeh Aghajani2 | ||
| 1M.Sc Computer Engineering, Faculty of Technology and Engineering, University of Mazandaran, Babolsar, Iran | ||
| 2Assistant Professor Department of Computer Engineering, Faculty of Technology and Engineering, University of Mazandaran, Babolsar, Iran | ||
| چکیده [English] | ||
| Network traffic analysis is a fundamental pillar in enhancing the security and efficient management of computer networks. Given the rapid growth of computer networks and the increasing complexity of their traffic, accurate and fast identification of various types of traffic, including encrypted traffic, has become crucial. In this context, the use of machine learning techniques offers a powerful tool for analyzing and accurately identifying traffic patterns. This paper examines advanced methods for traffic identification in computer networks using machine learning techniques. The primary goal of this research is to develop an efficient and accurate model for identifying and classifying various types of network traffic, particularly encrypted traffic. To achieve this, the deep learning model VGG16 was utilized. Due to its deep layered architecture and capability to analyze large volumes of data, VGG16 demonstrated outstanding performance in identifying complex network traffic patterns. It can accurately detect and classify different types of network traffic, thereby improving traffic management within networks. In the scenarios evaluated in this study, the model achieved a remarkable accuracy of 99% in identifying encrypted traffic. | ||
| کلیدواژه‌ها [English] | ||
| Deep Learning, Network Traffic Analysis, Encrypted Traffic, VGG16 model, Network Traffic Management | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 169 تعداد دریافت فایل اصل مقاله: 159 |
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