یک سیستم تشخیص حملات در اینترنت اشیاء با یادگیری عمیق مبتنی بر معماری VGG16 و الگوریتم بهینه سازی ببر سیبری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی کامپیوتر، گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران

2 استادیار گروه کامپیوتر، گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران

چکیده

یکی از چالشهای عمده در اینترنت اشیاء وجود گره‌های حمله کننده به نام بات نت است. در این حملات تعداد زیادی گره به بدافزار آلوده شده و بر علیه سرویس‌های شبکه حملاتی مانند رد سرویس خدمات توزیع شده را انجام می‌دهند. بات‌نتها در بیشتر موارد سرویس‌های کاربردی در لایه ابر محاسباتی را هدف قرار می‌دهند و از این جهت تشخیص حملات در اینترنت اشیاء به عنوان یک لایه میانی از اهمیت بالایی برخوردار است. ارایه یک سیستم تشخیص نفوذ توزیع شده در اینترنت اشیاء توانایی تشخیص نفوذ را افزایش می-دهد و توانایی بالایی برای تحلیل حجم زیاد ترافیک شبکه دارد. روشهای یادگیری عمیق نظیر شبکه عصبی کانولوشن توانایی بالایی برای تشخیص الگوهای پیچیده در تصاویر دارند. در این مقاله برای استفاده از معماری شبکه CNN در تشخیص نفوذ به شبکه، ترافیک شبکه به صورت تصاویر به شیوه جدید کدگذاری می‌شود. تصاویر ترافیک شبکه برای آموزش مدل VGG16 که یک تکنیک CNN است استفاده می‌شود. در روش پیشنهادی برای تمرکز سیستم تشخیص نفوذ پیشنهادی، از الگوریتم بهینه‌سازی ببر سیبری برای انتخاب ویژگی و کاهش ابعاد استفاده می‌شود. سیستم تشخیص نفوذ پیشنهادی روی مجموعه داده NSL-KDD آموزش داده می‌شود و ارزیابی‌ها نشان داد دارای دقت، حساسیت و صحتی به ترتیب برابر 62/99%، 38/99% و 74/98% است. روش پیشنهادی در فاز انتخاب ویژگی نسبت به الگوریتم‌ بهینه‌سازی وال، بهینه‌سازی شاهین و بهینه‌سازی عقاب طلایی دقت بیشتری دارد. روش پیشنهادی در تشخیص حملات به شبکه از روشهای CNN، VGG16، Multi-CNN و PSO-CNN دقت بیشتری دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

An attack detection system in Internet of Things with deep learning based on VGG16 architecture and Siberian tiger optimization algorithm (STO)

نویسندگان [English]

  • mohsen eghbali 1
  • Mohammadreza Mollahoseini Ardakani 2
  • Ahmad Heidary Sharifabad 2
1 PhD Student, Computer Engineering, Department of Computer Engineering, Meybod Branch, Islamic Azad University, Meybod, Iran
2 Associate Professor, Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran
چکیده [English]

One of the significant challenges in the Internet of Things is the presence of attacking nodes called botnets. Many nodes are infected with malware in these attacks and perform attacks against network services, such as distributed denial of service. In most cases, botnets target application services in the cloud computing layer. For this reason, it is essential to detect attacks in the Internet of Things as an intermediate layer. Providing a distributed intrusion detection system in the Internet of Things increases the ability to detect intrusion and has a high ability to analyze a large volume of network traffic. Deep learning methods such as convolutional neural networks have a high ability to recognize complex patterns in images. In this article, to use CNN network architecture in network intrusion detection, network traffic is coded in the form of images in a new way. Network traffic images are used to train the VGG16 model, a CNN technique. In the proposed method to focus the proposed penetration detection system, the Siberian tiger optimization algorithm is used to select features and reduce dimensions. The proposed intrusion detection system is trained on the NSL-KDD dataset. The evaluations showed that it has accuracy, sensitivity, and precision equal to 99.62%, 99.38%, and 98.74%, respectively. In the feature selection phase, the proposed method is more accurate than WOA, HHO, and AO algorithms. The proposed method is more accurate in detecting network attacks than CNN, VGG16, Muhti-CNN, and PSO-CNN methods.

کلیدواژه‌ها [English]

  • Internet of Things (IoT)
  • Intrusion Detection System (IDS)
  • Deep Learning
  • VGG16 Neural Network
  • Siberian Tiger Optimization (STO) Algorithm

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دوره 13، شماره 2 - شماره پیاپی 50
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صفحه 11-25

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