روش طبقه‌بندی بدافزار با استفاده از ویژگی های بصری سازی و تعبیه سازی کلمه براساس یادگیری عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکترا، دانشگاه سمنان، سمنان، ایران

2 دانشیار، دانشگاه سمنان، سمنان، ایران

3 استاد، دانشگاه سمنان، سمنان، ایران

چکیده

با رشد انفجاری تهدیدات برای امنیت اینترنت، بصری‌سازی بدافزارها در حوزه طبقه‌بندی بدافزارها به یک حوزه مطالعه امیدوار کننده در زمینه امنیت و یادگیری ماشین تبدیل شده است. این مقاله یک روش بصری‌سازی برای تجزیه و تحلیل بدافزار را بر اساس ویژگی‌های تعبیه‌سازی دنباله‌های کددستوری پیشنهاد می‌کند. بر اساس برخی اطلاعات کمکی مانند تعبیه‌سازی کلمه، روش اصلی طبقه‌بندی بدافزار پیشنهادی، انتقال اطلاعات آموخته شده از حوزه بدافزار به حوزه تصویر است که نیاز به مدل‌سازی همبستگی بین این حوزه‌ها دارد. با این حال، اکثر روش‌های فعلی از مدل‌سازی روابط غفلت می‌کنند که منجر به طبقه‌بندی نادرست بدافزارها می‌شود. برای غلبه بر این چالش، ما وظیفه تعبیه‌سازی کلمه را به عنوان استخراج اطلاعات معنایی در نظر می-گیریم. روش پیشنهادی یک روش طبقه‌بندی بدافزار با استفاده از مفاهیم تعبیه‌سازی کلمات و بصری‌سازی از توالی های کددستور و یک روش شبکه‌های عصبی شامل یادگیری عمیق (CNN) را پیشنهاد می‌کند. نتایج ما نشان می‌دهد که از مدل‌های بصری در حوزه تصاویر می‌توان برای طبقه‌بندی کارآمد بدافزارها استفاده کرد. ما روش خود را بر روی مجموعه داده kaggle ارزیابی کردیم و میانگین دقت طبقه‌بندی 0.9896 و امتیاز F1 برابر 0.9807 بدست آوردیم.

کلیدواژه‌ها


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