مقاله سوم تشخیص بات نت

 

چکیده

بات‌نت یک تهدید شبکه‌ای و اینترنتی است. برای جلوگیری سریع از بات‌نت‌ها، تحلیل و پایش آن‌ها ضروری است. اکثر رویکردها برای تشخیص بات‌ها با پردازش و پیش‌پردازش حجم زیادی از اطلاعات ورودی از بسته‌های شبکه، ساختارها و غیره پیشنهاد شده‌اند. رشد اخیر محیط‌های اینترنت و شبکه باعث رشد قابل توجه حملات بات‌نت شده است. بر این اساس، رویکردهای سنتی برای تشخیص بات‌نت مناسب نیستند. این مقاله رویکرد جدیدی برای تشخیص بات‌نت در شبکه‌ها ارائه می‌دهد. مدل تشخیص پیشنهادی برای مقایسه چهار حمله IRC، HTTP، DNS و P2P که توسط بات‌نت استفاده می‌شود، به کار می‌رود. علاوه بر این، این مدل دقت تشخیص بات‌نت را ارزیابی می‌کند. ما از شبکه‌های عصبی و همبستگی و همچنین الگوریتم انتخاب منفی (NSA) که مبتنی بر سیستم ایمنی مصنوعی است، برای شناسایی بات‌نت استفاده می‌کنیم و نتایج خود را با الگوریتم‌های جنگل تصادفی، K- همسایگی، SVM، بیز گاوسی، CNN و LSTM مقایسه می‌کنیم. روش ما (CNN-LSTM) زمان آموزش کوتاه‌تر و دقت بالاتری را ارائه می‌دهد. در این آزمایش، از مجموعه داده‌های بات‌نت ISOT و ISCX که به عنوان داده‌های ترافیک برچسب‌گذاری شده‌اند، استفاده می‌کنیم. علاوه بر این، انواع مختلف حملات بات‌نت را بررسی می‌کنیم و ارزیابی نهایی ارائه می‌شود.

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