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مجیدیان, سیده زهره, تقی پور عیوضی, شیوا, آراسته, بهمن, غفاری, علی. (1404). مدل طبقه‌بندی ترافیک شبکه‌های کامپیوتری مبتنی بر خوشه‌بندی DBScan و طبقه‌بند گاما. پدافند الکترونیکی و سایبری, (), -.
سیده زهره مجیدیان; شیوا تقی پور عیوضی; بهمن آراسته; علی غفاری. "مدل طبقه‌بندی ترافیک شبکه‌های کامپیوتری مبتنی بر خوشه‌بندی DBScan و طبقه‌بند گاما". پدافند الکترونیکی و سایبری, , , 1404, -.
مجیدیان, سیده زهره, تقی پور عیوضی, شیوا, آراسته, بهمن, غفاری, علی. (1404). 'مدل طبقه‌بندی ترافیک شبکه‌های کامپیوتری مبتنی بر خوشه‌بندی DBScan و طبقه‌بند گاما', پدافند الکترونیکی و سایبری, (), pp. -.
مجیدیان, سیده زهره, تقی پور عیوضی, شیوا, آراسته, بهمن, غفاری, علی. مدل طبقه‌بندی ترافیک شبکه‌های کامپیوتری مبتنی بر خوشه‌بندی DBScan و طبقه‌بند گاما. پدافند الکترونیکی و سایبری, 1404; (): -.

مدل طبقه‌بندی ترافیک شبکه‌های کامپیوتری مبتنی بر خوشه‌بندی DBScan و طبقه‌بند گاما

پدافند الکترونیکی و سایبری
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 02 مهر 1404    اصل مقاله (1.24 M)
نوع مقاله: مقاله پژوهشی
نویسندگان
سیده زهره مجیدیان1؛ شیوا تقی پور عیوضی* 2؛ بهمن آراسته3؛ علی غفاری4
1دانشجوی دکتری، گروه مهندسی کامپیوتر، واحد بین المللی ارس، دانشگاه آزاد اسلامی، تبریز، ایران
2استادیار، گروه مهندسی کامپیوتر، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران
3دانشیار، گروه مهندسی کامپیوتر، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران
4دانشیار،گروه مهندسی کامپیوتر، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران
تاریخ دریافت: 18 خرداد 1404،  تاریخ بازنگری: 18 مرداد 1404،  تاریخ پذیرش: 19 شهریور 1404 
چکیده
طبقه‌بندی ترافیک یکی از مهم‌ترین فرآیندهای نظارت بر شبکه است که کاربردهای گسترده‌ای در حوزه‌های امنیت، کیفیت خدمات و مدیریت شبکه دارد. با افزایش پیچیدگی و تنوع ترافیک شبکه، چالش‌های جدیدی از جمله کمبود داده‌های آموزشی برچسب‌گذاری‌شده بوجود می‌آید. به منظور رفع این چالش در این مقاله، سازوکار طبقه‌بندی ترافیک با ترکیب الگوریتم‌های یادگیری ماشین بدون نظارت و نیمه نظارتی ارائه می‌شود. این سازوکار از مجموعه محدودی از داده‌های آموزشی برچسب‌گذاری‌شده برای بهبود دقت طبقه‌بندی استفاده می‌کند. روش پیشنهادی، هر جریان ترافیک را به عنوان یک بردار ویژگی توصیف می‌کند که شامل ویژگی‌های آماری آن جریان است. تعداد ویژگی‌های ایجاد شده برای هر نمونه نیز با استفاده از تحلیل مولفه‌های اصلی کاهش می‌یابد. خوشه‌بندی DBScan برای تعیین نوع ترافیک صحیح برای هر جریان ترافیک بدون برچسب استفاده می‌شود. در نهایت، از مدل طبقه‌بند گاما برای تفکیک جریان‌های ترافیک جدید استفاده می‌شود. کارایی روش پیشنهادی با استفاده از مجموعه داده‌های واقعی ارزیابی شده است. نتایج نشان می‌دهد که روش پیشنهادی قادر به طبقه‌بندی جریان‌های ترافیکی با دقت متوسط 95.12 درصد است که حداقل 7.03 درصد بهبود را نسبت به رویکردهای قبلی نشان می‌دهد.

کلید واژه‌ها: طبقه بندی ترافیک، یادگیری ماشین، خوشه بندی DBScan، طبقه‌بند گاما.
کلیدواژه‌ها
طبقه بندی ترافیک؛ یادگیری ماشین؛ خوشه بندی DBScan؛ طبقه‌بند گاما
موضوعات
امنیت اطلاعات، رمزنگاری، پنهان نگاری، پروتکل ها و استانداردها
عنوان مقاله [English]
Computer NetworksTraffic Classification Model Based on DBScan Clustering and Gamma Classification
نویسندگان [English]
Seyede Zohreh Majidian1؛ Shiva TaghipourEivazi2؛ Bahman Arasteh3؛ Ali Ghaffari4
1PhD student. Department of Computer Engineering, Aras international Branch, Islamic Azad University, Tabriz, Iran
2assistant professor. Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
3Associate Professor. Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
4Associate Professor . Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
چکیده [English]
Traffic classification is one of the most important network monitoring processes that has wide applications in the fields of security, quality of service, and network management. With the increasing complexity and variety of network traffic, new challenges arise, including the lack of labeled training data. In order to solve this challenge, in this paper, a traffic classification mechanism is presented by combining unsupervised and semi-supervised machine learning algorithms. This mechanism uses a limited set of labeled training data to improve classification accuracy. The proposed method describes each traffic flow as a feature vector that contains the statistical characteristics of that flow. The number of features generated for each sample is reduced using principal component analysis. DBScan clustering is used to determine the correct traffic type for each untagged traffic stream. Finally, the gamma classifier model is used to separate the new traffic flows. The efficiency of the proposed method has been evaluated using real data sets. The results show that the proposed method is able to classify traffic flows with an average accuracy of 95.12%, which shows at least 7.03% improvement over previous approaches.
کلیدواژه‌ها [English]
Traffic classification, Machine learning, DBScan clustering, Gamma classification
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