یک رویکرد نوین برای سیستم تشخیص نفوذ در اینترنت اشیاء با استفاده از انتخاب ویژگی ترکیبی مبتنی بر همبستگی و الگوریتم بهینه‌سازی شاهین هریس
محورهای موضوعی : شبکه های عصبی و یادگیری عمیق
یاشار سلامی
1
,
یاسر عبازاده
2
,
مهدی همرنگ
3
,
نوشین الله بخشی
4
1 - Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - گروه مهندسی کامپیوتر، واحد گرمی، دانشگاه آزاد اسلامی، گرمی، ایران.
3 - گروه مهندسی کامپیوتر، واحد گرمی، دانشگاه آزاد اسلامی، گرمی، ایران
4 - Department of Computer and Information Technology Engineering, Khoy Branch, Islamic Azad University, Khoy, Iran
کلید واژه: اینترنت اشیا , تشخیص نفوذ, بهینه سازی , الگوریتم ,
چکیده مقاله :
با رشد سریع اینترنت اشیاء (IoT)، تعداد دستگاه‌های متصل به شبکه‌های مختلف به طور قابل توجهی افزایش یافته است. این دستگاه‌ها مقادیر زیادی داده تولید می‌کنند و غالباً در محیط‌های باز و ناامن مستقر می‌شوند که آن‌ها را در معرض حملات سایبری مختلف قرار می‌دهد. بنابراین، اطمینان از امنیت شبکه‌های IoT به یک نگرانی اصلی برای محققان تبدیل شده است. یکی از مؤثرترین روش‌ها برای حفظ امنیت شبکه، استفاده از سیستم‌های تشخیص نفوذ (IDS) است. تشخیص نفوذ، داده‌های ورودی را نظارت و تحلیل می‌کند تا فعالیت‌های مشکوک و حملات احتمالی را شناسایی کند. با توجه به محدودیت‌های منابع دستگاه‌های IoT و پیچیدگی شبکه‌ها، بهبود دقت و کارآیی IDS بسیار حائز اهمیت است. هدف اصلی این تحقیق، ارائه یک IDS جدید و بهینه‌سازی شده برای شبکه‌های IoT است. یک روش انتخاب ویژگی ترکیبی برای افزایش دقت و کاهش پیچیدگی محاسباتی به کار گرفته شده است که شامل فیلتر کردن مبتنی بر همبستگی و روش‌های پوششی با استفاده از الگوریتم بهینه‌سازی شاهین هریس (HHO) می‌باشد. در این رویکرد، ویژگی‌های غیرضروری حذف و ویژگی‌های ضروری برای طبقه‌بندی انتخاب می‌شوند. نتایج شبیه‌سازی نشان می‌دهد که این روش به دقت 96.46% دست یافته است و در مقایسه با روش‌های سنتی مانند DT و SVM عملکرد بهتری داشته و نرخ‌های مثبت کاذب و منفی کاذب را بهبود بخشیده است.
With the rapid growth of the Internet of Things (IoT), the number of devices connected to various networks has significantly increased. These devices generate vast amounts of data and are often deployed in open and unsecured environments, making them vulnerable to various cyber-attacks. Therefore, ensuring the security of IoT networks has become a primary concern for researchers. One of the most effective methods for maintaining network security is using Intrusion Detection Systems (IDS). Intrusion detection monitors and analyzes incoming data to detect suspicious activities and potential attacks. Given the resource constraints of IoT devices and the complexity of the networks, improving the accuracy and efficiency of IDS is crucial. The primary goal of this research is to present a novel and optimized IDS for IoT networks. A hybrid feature selection method has been employed to enhance accuracy and reduce computational complexity, combining correlation-based filtering and wrapper methods using the Harris Hawk Optimization (HHO) algorithm. In this approach, unnecessary features are removed, and essential features for classification are selected. Simulation results indicate that this method has achieved a 96.46% accuracy, outperforming traditional methods such as DT and SVM while improving false positive and false negative rates
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