ارائه رویکردی جدید برای تشخیص حملات علیه صدا از طریق پروتکل اینترنت مبتنی بر خوشه‌بندی تجمیعی

محورهای موضوعی : شبکه های کامپیوتری و امنیت

فرید باوی فرد ¹ , محمد خیراندیش ² , محمد مصلح ³

1 - گروه مهندسی کامپیوتر- واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران
2 - گروه مهندسی کامپیوتر- واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران
3 - گروه مهندسی کامپیوتر- واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران

تاریخ دریافت : 1401/07/23 تاریخ پذیرش : 1401/11/22 تاریخ انتشار : 1404/05/15

کلید واژه: الگوريتم بهینه‌سازی, انتخاب ویژگي, پرسپترون چندلایه, خوشه‌بندی تجمیعی, سیستم تشخیص نفوذ, شبیه‌‌سازی تبرید ,

چکیده مقاله :

با توجه به هزینه کمتر و انعطاف‌پذیری بیشتر، انتقال صدا از طریق پروتکل اینترنت (VoIP) به طور گسترده‌ای در ارتباطات راه دور استفاده می‌شود. تنوع پایانه‌های VoIP باعث آسیب‌پذیری آنها می‌شود. یک راه متداول برای ایمن‌سازی VoIP، شامل تشخیص نفوذ مبتنی بر یادگیری ماشین است. با توجه به تنوع ترافیک و عدم وجود برچسب کلاس برای آموزش سیستم‌های تشخیص نفوذ (IDS) در بسیاری از مواقع، بر رویکردهای خوشه‌بندی (یادگیری بدون ناظر) متمرکز شده‌اند. اما سیستم‌های خوشه‌بندی منفرد نمی‌توانند تنوع مقادیر ویژگی‌ها را به­خوبی پوشش دهند و برخی از نمونه‌های ترافیک ممکن است به­عنوان نقاط پرت شناسایی شوند. مدل پیشنهادی، به‌عنوان یک رویکرد تجمیعی برای حل این مسائل، روی استفاده از الگوریتم خوشه‌بندی دومرحله‌ای متمرکز شده و سعی می‌کند با ایجاد بهبودی در آن، فرآیند تشخیص نفوذ مبتنی بر خوشه‌بندی را بهبود دهد. علاوه بر این، با توجه به اهمیت فرآیند انتخاب ویژگی، ترکیبی از الگوریتم شبیه‌سازی تبرید (SA) و شبکه عصبی پرسپترون چندلایه (MLP)، برای شناسایی ویژگی‌های برتر مورد استفاده در خوشه‌بندی بسته‌های VoIP، در قالب بسته‌های عادی یا حمله انکار سرویس (DoS)، حمله کاربر به ریشه (U2R)، حمله کاربر از راه دور (R2L) و حمله پویش‌گر مورد بهره‌‌برداری قرار گرفته است. بر اساس نتایج ارزیابی بر روی مجموعه داده "آزمایشگاه امنیت شبکه– کشف دانش در پایگاه‌های داده‌ای" (­NSL-KDD)، توسط نرم‌افزار متلب، انتخاب ویژگی پیشنهادی با کاهش ویژگی‌ها به 10 و 8، زمان آموزش و آزمایش را به‌ترتیب 77 درصد و 80 درصد کاهش می‌دهد. همچنین در مقایسه با تعدادی از مطالعات قبلی، IDS پیشنهادی بهبود متوسطی معادل 34/3 درصد، 17/14 درصد و 87/32 درصد را به‌ترتیب در دقت، نرخ تشخیص و معیار F نشان می‌دهد.

 

 

 

چکیده انگلیسی:

Due to lower cost and greater flexibility, voice over internet protocol (VoIP) is widely used in telecommunications. A variety of VoIP terminals causes them to be vulnerable. A common way to secure VoIP includes intrusion detection based on machine learning. Due to the diversity of traffics and lack of class labels for training Intrusion detection systems (IDSs) in many situations, clustering approaches (unsupervised learning) have been focused on. But individual cluster systems can't cover the diversities of feature values well, and some traffic samples may be identified as outliers. As an ensemble approach, the proposed model for solving these problems focuses on using TwoStep clustering algorithm, and by improving it, tries to improve the clustering-based intrusion detection. Moreover, regarding the importance of the feature selection process, a combination of Simulated Annealing algorithm (SA) and Multi-Layer Perceptron (MLP) has been exploited for identifying superior features used for clustering VoIP packets, as Normal or involving DoS, R2L, U2R either Probe attacks. Based on evaluation results obtained on the dataset “Network Security Lab-Knwledge Discovery in Databases” (NSL-KDD) by MATLAB, the proposed feature selection reduced the training and testing times, averagely by 77% and 80%, respectively, by reducing the features to 10 and 8. Also, compared to previous works, the proposed IDS shows average improvements in Accuracy, Detection rate, and F-Measure at 3.34 %, 14.17 %, and 32.87 %, respectively.

 

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