تشخیص حملات در زیرساخت اینترنت اشیاء با استفاده از الگوریتم بهبودیافته شامپانزه و یادگیری عمیق

زارع فرخادی, رویا; مجیدزاده, کامبیز; مصدری, محمد; غفاری, علی

doi:10.47176/ECDJ.2025.1588

تشخیص حملات در زیرساخت اینترنت اشیاء با استفاده از الگوریتم بهبودیافته شامپانزه و یادگیری عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان

¹ دانشجوی دکتری، گروه مهندسی کامپیوتر، دانشکده کامپیوتر، دانشگاه آزاد اسلامی،ارومیه، ایران

² استادیار،گروه مهندسی کامپیوتر، دانشکده کامپیوتر، دانشگاه آزاد اسلامی،ارومیه، ایران

³ دانشیار،گروه مهندسی کامپیوتر، دانشکده کامپیوتر، دانشگاه آزاد اسلامی،ارومیه، ایران

10.47176/ECDJ.2025.1588

چکیده

افزایش تعداد دستگاه‌های اینترنت اشیا، سرعت‌بالا و حجم زیاد اطلاعات تولیدشده، باعث شده است که مسئله امنیت شبکه‌های اینترنت اشیا و شناسایی حملات سایبری در این شبکه‌ها، به یکی از چالش‌های مهم در این حوزه تبدیل شود. سامانه‌های تشخیص نفوذ به‌عنوان یکی از راهکارهای ارائه‌شده برای مقابله با این مشکل است. انتخاب صحیح ویژگی‌ها در ایجاد مدل‌های تشخیص نفوذ می‌تواند باعث افزایش چشمگیری در دقت تشخیص شود. در این مقاله الگوریتم دودویی و بهبودیافته شامپانزه‌ها برای انتخاب ویژگی طراحی‌شده است. الگوریتم شامپانزه برای حل مسائل پیوسته است و در حل مسائل دودویی نمی‌تواند کارآمد باشد. همچنین دارای مشکل افتادن در دام محلی است و اکتشاف و بهره‌وری و همگرایی در این الگوریتم کند است. بنابراین نیاز است تغییراتی در این الگوریتم برای حل مسائل دودویی انجام شود. ازاین‌رو در این مقاله یک نسخه بهبودیافته شامپانزه برای مسائل گسسته و انتخاب ویژگی و رفع موارد ذکرشده، در تشخیص نفوذ و حملات مبتنی بر شبکه‌های اینترنت اشیا طراحی و پیاده‌سازی شده است. روش پیشنهادی به‌طور میانگین 60 درصد ویژگی‌ها را کاهش داده و به ترتیب با دقت‌های3/99 ،6/99 و9/99درصد در مجموعه داده‌های ToN-IoT، UNSW-NB15 و IoTID20 ، موفق به تشخیص حملات شده است و به‌طور چشمگیری باعث کاهش نرخ هشدار کاذب حملات شده است. تحلیل آماری آزمون کراس کال واریس نشان داد که روش پیشنهادی نسبت به روش‌های مورد مقایسه با سرعت بیشتری همگرا می‌شود

کلیدواژه‌ها

موضوعات

آسیب پذیری ها و تهدیدات فضای سایبری

عنوان مقاله [English]

Detecting attacks in Internet of Things infrastructure using improved chimpanzee algorithm and deep learning

نویسندگان [English]

roya zareh farkhady ¹
kambiz majidzadeh ²
mohammad masdari ²
Ali Ghaffari ³

¹ PhD student, Department of Computer Engineering, Faculty of Computer Science, Islamic Azad University, Urmia, Iran

² Assistant Professor, Department of Computer Engineering, Faculty of Computer Science, Islamic Azad University, Urmia, Iran

³ Associate Professor, Department of Computer Engineering, Faculty of Computer Science, Islamic Azad University, Urmia, Iran

چکیده [English]

The increase in the number of Internet of Things devices, high speed, and large volume of generated data has led to network security issues and the identification of cyber-attacks in these networks becoming one of the key challenges in this area. Intrusion detection systems have been proposed as a solution to tackle this problem. Proper selection of features in creating intrusion detection models can significantly increase detection accuracy. In this article, a binary algorithm and an improved chimpanzee algorithm have been designed for feature selection. The chimpanzee algorithm is designed for solving continuous problems and cannot be efficient in solving binary problems. It also suffers from local optima and slow exploration, exploitation, and convergence in this algorithm. Therefore, changes need to be made in this algorithm to solve binary problems. Hence، in this article an improved version of the chimpanzee algorithm for discrete problems and feature selection has been designed and implemented for intrusion detection and network-based attacks in Internet of Things networks. The proposed method reduces features by an average of 60 percent and successfully detects attacks with ac curacies of 99.3%, 99.6%, and 99.9% in the Ton-IoT، UNSW-NB15، and IoTID20 datasets, significantly reducing the false alarm rate of detected attacks. Statistical analysis using the Kruskal-Wallis test showed that the proposed method converges faster compared to the comparison methods.

کلیدواژه‌ها [English]

Internet of Things
Feature Selection
Deep Learning

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