پدافند الکترونیکی و سایبری

پدافند الکترونیکی و سایبری

ارائه مدل ترکیبی و هوشمند برای تشخیص ناهنجاری در اینترنت اشیا با رویکرد یادگیری گروهی و رمزگذارهای عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکتری ، گروه مهندسی کامپیوتر ، واحد یاسوج، دانشگاه آزاد اسلامی، یاسوج، ایران
2 دانشیار ، گروه مهندسی کامپیوتر ، واحد یاسوج، دانشگاه آزاد اسلامی، یاسوج، ایران
3 دانشیار ، گروه مهندسی برق ، واحد یاسوج، دانشگاه آزاد اسلامی، یاسوج، ایران
4 دانشیار ، گروه مهندسی کامپیوتر ، واحد نورآباد ممسنی، دانشگاه آزاد اسلامی، نورآباد ممسنی ، ایران
5 استادیار ، گروه مهندسی کامپیوتر ، واحد یاسوج، دانشگاه آزاد اسلامی، یاسوج، ایران
چکیده
اینترنت اشیا (IoT) با فراهم کردن بستری برای ارتباط خودکار میان میلیون‌ها دستگاه هوشمند، به یکی از زیرساخت‌های حیاتی دنیای مدرن تبدیل‌شده است. رشد چشمگیر تعداد این دستگاه‌ها و افزایش ویژگی‌هایی مانند تنوع، مقیاس‌پذیری و تحریک‌پذیری آن‌ها، تهدیدات امنیتی را نیز به‌طور قابل‌توجهی گسترش داده است. به‌ویژه ظهور تهدیدات پنهان و پیچیده که از طریق الگوهای رفتاری غیرمعمول در شبکه بروز می‌کنند، ضرورت بهره‌گیری از روش‌های پیشرفته تشخیص ناهنجاری را دوچندان کرده است.در این مقاله، مدلی نوین مبتنی بر یادگیری گروهی ارائه‌شده است که با ترکیب تکنیک‌های یادگیری عمیق و الگوریتم‌های یادگیری ماشین، عملکرد تشخیص ناهنجاری‌ها در اینترنت اشیا را بهبود می‌بخشد. پس از انجام فرایند پیش‌پردازش داده‌ها، ویژگی‌های کلیدی با استفاده از رمزگذار خودکار پشته‌ای (SAE) و رمزگذار خودکار عمیق (DAE) استخراج و در مرحله بعد چارچوب یادگیری گروهی متشکل از درخت تصمیم (DT)، پرسپترون چندلایه (MLP)، شبکه عصبی احتمالی (PNN) و نسخه وزنی آن (WPNN) برای شناسایی ناهنجاری‌ها به کار گرفته‌شده است.از نوآوری‌های این تحقیق می‌توان به طراحی سازوکار انتخاب پویای ویژگی در لایه رمزگذار و همچنین پیشنهاد طرح وزن دهی تطبیقی برای ترکیب خروجی مدل‌های گروهی اشاره کرد که منجر به بهبود دقت در تشخیص حملات ناشناخته و کاهش نرخ مثبت کاذب شده است.مدل پیشنهادی بر روی چندین مجموعه داده معتبر ازجمله NSL-KDD، BoT-IoT، IoT-NI، IoT-23 ارزیابی و نتایج نشان‌دهنده دقت بالای ۹۹.۳٪ و امتیاز F1 معادل ۹۹.۲٪ در مجموعه داده ترکیبی IoT-DS2 است. نرخ مثبت کاذب (FPR) در اغلب مجموعه داده‌ها زیر ۲٪ و نرخ منفی کاذب (FNR) کمتر از ۱.۵٪ گزارش‌شده است. در مقایسه با مدل‌های منتخب مدل پیشنهادی در شناسایی حملات ناشناخته برتری دارد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

A Hybrid and Intelligent Model for Anomaly Detection in Internet of Things Using Ensemble Learning and Deep Autoencoders

نویسندگان English

hadi tarazodar 1
Karamollah Bagherifard 2
Samad Nejatian 3
Hamid Parvin 4
Razieh Malekhosseini 5
1 Ph.D. Student, Department of computer Engineering ,Yas.C., Islamic Azad University ,Yasuj, Iran,
2 Associate Professor, Department of computer Engineering ,Yas.C., Islamic Azad University ,Yasuj, Iran
3 Assistant Professor, Department of Electrical Engineering ,Yas.C., Islamic Azad University ,Yasuj, Iran
4 Associate Professor, Department of computer Engineering , NoM.C., Islamic Azad University , Noorabad Mamasani, Iran
5 Assistant Professor, Department of computer Engineering ,Yas.C., Islamic Azad University ,Yasuj, Iran
چکیده English

The Internet of Things (IoT) has become a critical infrastructure of the modern world by providing a platform for automated communication between millions of smart devices. The dramatic growth in the number of these devices and the increase in their features such as diversity, scalability, and mobility have also significantly expanded security threats. In particular, the emergence of hidden and complex threats that manifest through unusual behavioral patterns in the network has doubled the need for advanced anomaly detection methods. In this paper, a new model based on ensemble learning is presented that improves the anomaly detection performance in the Internet of Things by combining deep learning techniques and machine learning algorithms. After performing the data preprocessing process, key features were extracted using Stacked Auto Encoder (SAE) and Deep Auto Encoder (DAE), and in the next step, an ensemble learning framework consisting of a decision tree (DT), a multilayer perceptron (MLP), a probabilistic neural network (PNN), and its weighted version (WPNN) was used to identify anomalies. Among the innovations of this research are the design of a dynamic feature selection mechanism in the encoder layer and the proposal of an adaptive weighting scheme for combining the output of ensemble models, which has led to improved accuracy in detecting unknown attacks and reduced false positive rates. The proposed model was evaluated on several valid datasets, including NSL-KDD, BoT-IoT, IoT-NI, IoT-23, and the results show a high accuracy of 99.3% and an F1 score of 99.2% in the combined IoT-DS2 dataset. The false positive rate (FPR) is reported to be below 2% and the false negative rate (FNR) is reported to be below 1.5% in most datasets. Compared to the selected models, the proposed model is superior in detecting unknown attacks.

کلیدواژه‌ها English

Internet of Things
Anomaly Detection
Stacked Autoencoder
Deep Autoencoder
Ensemble Learning
Dynamic Feature Selection Adaptive combination of models
Cybersecurity
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دوره 13، شماره 4 - شماره پیاپی 52
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زمستان 1404
صفحه 103-123

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