استفاده از یادگیری انتقالی برای افزایش امنیت سایبری در شبکه‌های اینترنت اشیا

ملخص المقالة :

سیستم‌های تشخیص نفوذ (IDS) با شناسایی دسترسی غیرمجاز شبکه‌های کامپیوتری و بررسی نقض‌های امنیتی احتمالی، نقش اساسی در دفاع از شبکه‌های کامپیوتری ایفا می‌کنند. IDS های سنتی برای مبارزه با حملات امنیتی پیچیده جدید ایجاد شده با مشکلاتی مواجه می‌شوند. از این رو، نیاز روزافزونی به راه‌حل تشخیص نفوذ خودکار وجود دارد که می‌تواند فعالیت‌های مخرب را با دقت بیشتری شناسایی کند. در این مقاله، مدل جدید تشخیص نفوذ شبکه اینترنت اشیا با استفاده از یک شبکه عصبی بر اساس معماری VGG16 از پیش آموزش دیده پیشنهاد شده است. مدل یک فرآیند دو مرحله‌ای است که در مرحله اول برخی از لایه‌ها VGG16 را از پیش بر روی مجموعه داده‌ی ImageNet آموزش داده‌ شده و وزن‌های آن را انتقال می‌دهیم و در مرحله دوم به طور خاص برای تشخیص نفوذ در شبکه‌های اینترنت اشیا عمل  Fine Tuning  انجام ‌می‌شود. زیرا این شبکه‌ها به دلیل محدودیت‌های منابع، پروتکل‌های منحصر به فرد و تنوع دستگاه‌ها، آسیب‌پذیری‌های خاصی دارند که نیازمند روش‌های تشخیص نفوذ مختص به خود هستند. مدل روی مجموعه داده‌های UNSW-NB15 و NSL-KDD ، اعمال شده است. نتایج نشان می‌دهد که روش پیشنهاد شده می‌تواند به طور مؤثر با تولید عملکرد واقعی از نظر accuracy، Precision، Recall و F1_score یاد بگیرد.

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