یک رویکرد تشخیص حملات توزیع شده در لایه مه و بر اساس پایگاه داده توزیع شده بلاک چین و یادگیری ماشین
محورهای موضوعی : مهندسی کامپیوتر
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)
کلید واژه: سیستم تشخیص نفوذ, لایه مه, یادگیری ماشین, شبکه عصبی GAN, انتخاب ویژگی, الگوریتم بهینه¬سازی کوآتی.,
چکیده مقاله :
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.
DDoS attacks make network services unavailable to users by sending fake traffic by botnets. One of the methods to deal with DDoS attacks is to use machine learning, but these methods face challenges such as high volume of IoT traffic and data imbalance. This paper introduces a distributed intrusion detection system in the fog layer that detects network attack traffic in a decentralized manner. In this method, each fog node acts as an intrusion detection system, and by exchanging blacklists through the blockchain, they increase the secrecy of detecting attacks. Fog nodes identify the main features of network traffic using the Coati optimization algorithm and use these features to train a multilayer neural network in intrusion detection. The selection of features reduces traffic and increases the accuracy and speed of attack detection. Based on game theory, the GAN method is used to balance network traffic. Tests performed in the MATLAB and on the NSL-KDD show that the proposed system has accuracy, sensitivity, and precision of 98.67%, 98.52%, and 98.34%, respectively. This method is more accurate in identifying network attacks than feature selection methods such as WOA, GWO, and HHO and more accurate than LSTM and CNN.
متعادل سازی ترافیک شبکه در لایه مه با تئوری بازی مبتنی بر شبکه GAN
ارایه یک نسخه باینری از الگوریتم بهینهسازی کوآتی که در سال 2023 ارایه شده برای انتخاب ویژگی
حفظ محرمانگی سیستم تشخیص نفوذ پیشنهادی با بلاک چین و مبادله لیست سیاه با بلاک چین بین گرههای مه
ارایه یک سیستم تشخیص نفوذ توزیع شده در لایه مه برای تشخیص حملات به IoT
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