تشخیص شبکه‌بات نظیربه‌نظیر با استفاده از روش یادگیری عمیق

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکترا، گروه مهندسی کامپیوتر، واحد شبستر، دانشگاه آزاد اسلامی، شبستر، ایران

2 تهران، اتوبان رسالت، خیابان هنگام، دانشگاه علم و صنعت ایران

3 استادیار، گروه مهندسی کامپیوتر، واحد شبستر، دانشگاه آزاد اسلامی، شبستر، ایران

چکیده

یک شبکه‌بات، شبکه­ای از رایانه‌های آلوده و دستگاه­های هوشمند بر روی اینترنت است که توسط مدیر‌بات بد­افزار از راه دور کنترل می‌شود تا فعالیت­های بدخواهانه مختلفی نظیر اجرای حملات منع خدمات، ارسال هرزنامه، سرقت کلیک و غیره را انجام دهند. زمانی­که مدیربات با بات‌های خود ارتباط برقرار می­کند، ترافیکی تولید می­کند که تجزیه و تحلیل این ترافیک برای شناسایی ترافیک شبکه­بات می­تواند یکی از عوامل تاثیر گذار برای سامانه­های تشخیص نفوذ باشد. در این مقاله، روش یادگیری عمیق با حافظه کوتاه‌مدت ماندگار (LSTM) جهت طبقه‌بندی فعالیت­های شبکه‌بات نظیر­به­نظیر پیشنهاد می­شود. رویکرد پیشنهادی بر اساس ویژگی­های بسته­های پروتکل­کنترل­انتقال بوده و کارایی روش با استفاده از دو مجموعه داده ISCX و ISOT ارزیابی می‌شود. نتایج آزمایش‌های انجام‌یافته، توانایی بالای رویکرد پیشنهادی برای شناسایی فعالیت­های شبکه‌بات نظیر­به­نظیر را بر اساس معیارهای ارزیابی نشان می­دهد. روش پیشنهادی نرخ دقت 65/99‌ درصد، نرخ صحت 32/96 درصد و نرخ بازخوانی 63/99 درصد را با نرخ مثبت کاذب برابر 67/0 ارائه می­کند.

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