یک رویکرد تشخیص حملات توزیع شده در لایه مه و بر اساس پایگاه داده توزیع شده بلاک چین و یادگیری ماشین
الموضوعات :
محسن اقبالی
1
(دانشجوی دکتری مهندسی کامپیوتر، گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران)
محمدرضا ملاحسینی اردکانی
2
(گروه کامپیوتر، دانشگاه آزاد اسلامی واحد میبد، میبد، ایران)
الکلمات المفتاحية: سیستم تشخیص نفوذ, لایه مه, یادگیری ماشین, شبکه عصبی GAN, انتخاب ویژگی, الگوریتم بهینه¬سازی کوآتی.,
ملخص المقالة :
حملات DDoS با ارسال حجم زیادی از ترافیک کاذب توسط بات‌نت‌ها، سرویس‌های شبکه را از دسترس کاربران خارج می‌کنند. یکی از روش‌های مقابله با حملات DDoS، استفاده از یادگیری ماشین است، اما این روش‌ها با چالش‌هایی مانند حجم بالای ترافیک IoT و عدم توازن در داده‌ها مواجه‌اند. این مقاله سیستم تشخیص نفوذ توزیع‌شده‌ای در لایه مه معرفی می‌کند که به‌صورت غیرمتمرکز ترافیک حملات شبکه را شناسایی می‌کند. در این روش، هر گره مه به عنوان سیستم تشخیص نفوذ عمل کرده و با تبادل لیست سیاه‌ها از طریق بلاکچین، محرمانگی شناسایی حملات را افزایش می‌دهند. گره های مه ویژگی‌های اصلی ترافیک شبکه را با استفاده از الگوریتم بهینه‌سازی کوآتی(Coati) شناسایی کرده و از این ویژگی‌ها برای آموزش شبکه عصبی چندلایه ا در تشخیص نفوذ استفاده می‌کنند. انتخاب ویژگی‌ها ترافیک را کاهش داده و دقت و سرعت شناسایی حملات را افزایش می‌دهد. برای تعادل ترافیک شبکه، از روش GAN بر اساس نظریه بازی‌ها استفاده می‌شود. آزمایش‌ها در محیط نرم‌افزاری متلب و روی NSL-KDD نشان می‌دهد که سیستم پیشنهادی دارای دقت، حساسیت و صحتی به ترتیب 98.67%، 98.52% و 98.34% است. این روش در شناسایی حملات شبکه دقیق‌تر از روش‌های انتخاب ویژگی مانند WOA، GWO و HHO و نیز دقیق‌تر از LSTM وCNN است.
Network traffic balancing in fog layer with game theory based on GAN network
Presenting a binary version of the Kuati optimization algorithm presented in 2023 for feature selection
Maintaining the confidentiality of the proposed intrusion detection system with blockchain and exchanging the blacklist with blockchain between fog nodes
Providing a distributed intrusion detection system in the fog layer to detect attacks on IoT
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