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| تعداد مقالات | 8,812 |
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تشخیص بات‌نت‌ها با استفاده از فنون یادگیری عمیق | ||
| پدافند الکترونیکی و سایبری | ||
| دوره 11، شماره 2 - شماره پیاپی 42، تیر 1402، صفحه 31-43 اصل مقاله (1.5 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| مریم قنواتی نسب1؛ مهدیه قزوینی* 2؛ فهیمه قاسمیان3 | ||
| 1دانشجوی کارشناسی ارشد، دانشگاه شهید باهنر کرمان، کرمان، ایران | ||
| 2دانشیار، دانشگاه شهید باهنر کرمان، کرمان، ایران | ||
| 3استادیار، دانشگاه شهید باهنر کرمان، کرمان، ایران | ||
| تاریخ دریافت: 22 خرداد 1401، تاریخ بازنگری: 30 دی 1401، تاریخ پذیرش: 27 اردیبهشت 1402 | ||
| چکیده | ||
| امروزه به دلیل اتصال تلفن‌های همراه هوشمند به اینترنت و وجود قابلیت‌ها و امکانات مختلف در این تلفن‌ها، حفظ امنیت این دستگاه‌ها به یک چالش مهم تبدیل شده است. چرا که معمولا در این دستگاه‌ها انواع داده‌های خصوصی که مرتبط با حریم شخصی افراد است ثبت و ذخیره می‌شود. در سال‌های اخیر این دستگاه‌ها مورد هدف یکی از خطرناک‌ترین حملات سایبری قرار گرفته‌اند که بات‌نت نام دارد. بات‌نت‌ها توانایی انجام عملیات مخربی چون ربودن و استراق سمع و حملات انکار سرویس را دارند. از این‌رو شناسایی به موقع بات‌نت‌ها تاثیر زیادی در حفظ امنیت تلفن‌های همراه دارد. در این مقاله روشی جدید برای شناسایی بات‌نت‌ها از برنامههای سالم اندروید و همچنین تشخیص نوع بات‌نت از میان 14 نوع مختلف از خانواده بات‌نت‌ها ارائه شده است. در این روش ابتدا با استفاده از مهندسی معکوس، لیست مجوزهای برنامه استخراج شده، سپس بر اساس این لیست مجوز‌ها تصویر معادل برنامه ایجاد می‌شود. به این ترتیب مجموعه‌ای از تصاویر بدست می‌آید که با استفاده از شبکه عصبی کانولوشنال ارائه شده، این تصاویر طبقه‌بندی و نوع برنامه کاربردی مشخص می‌شود. نتایج حاصل از مقایسه و ارزیابی این روش با روش‌های سنتی یادگیری ماشین چون ماشین بردار پشتیبان و درخت تصمیم نشان داد که روش ارائه شده کارایی بالاتری در تشخیص انواع بات‌نت‌ها و جداسازی آن از برنامههای سالم دارد | ||
| کلیدواژه‌ها | ||
| بات‌نت؛ امنیت تلفن همراه؛ امنیت؛ بات‌نت تلفن همراه؛ تشخیص بات‌نت؛ شبکه کانولوشن | ||
| عنوان مقاله [English] | ||
| Mobile botnets detection using deep learning techniques | ||
| نویسندگان [English] | ||
| Maryam Ghanavati Nasab1؛ Mahdieh Ghazvini2؛ Fahimeh Ghasemian3 | ||
| 1Master's student, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| 2Associate Professor, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| 3Assistant Professor, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| چکیده [English] | ||
| Smartphones are now well integrated with advanced capabilities and technologies such as the Internet. Today, due to the facilities and capabilities and the widespread use of smart mobile devices, mobile security has become a vital issue worldwide. Smartphones are not properly protected compared to computers and computer networks, and users do not consider security updates. Recently, mobile devices and networks have been targeted by one of the most dangerous cyber threats known as botnets. Mobile Bantent An enhanced example of Boutons has the ability to perform malicious operations such as denial of service attacks, data theft, eavesdropping, and more. Bunters use three communication protocols: HTTP, SMS and Bluetooth to communicate with each other; So when users are not connected to the Internet, botnets are able to communicate with each other. In this study, to identify mobile batonet from 14 Android baton families, including 1932 samples of Android mobile devices applications and 4304 samples of safe and secure Android mobile devices applications have been used. Application permissions were extracted for reverse engineering to automatically classify and detect types of botnets, then based on these permissions, each application was converted to an equivalent image using the proposed method. Labeled images were then used to train convolutional neural networks. The results of evaluation and comparison of this method with classical methods including backup vector machine and decision tree showed that the proposed method is able to achieve higher efficiency in detecting different types of botnets and separating it from healthy programs | ||
| کلیدواژه‌ها [English] | ||
| Botnet, mobile security, security, mobile botnet, botnet detection, convolutional network | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 223 تعداد دریافت فایل اصل مقاله: 250 |
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