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تشخیص هوشمند دامنه‌های مشکوک از داده‌های DNS | ||
| پدافند الکترونیکی و سایبری | ||
| دوره 9، شماره 3 - شماره پیاپی 35، آذر 1400، صفحه 83-97 اصل مقاله (1.08 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| محسن رضوانی1؛ فهیمه باقری* 2؛ منصور فاتح3؛ اسماعیل طحانیان1 | ||
| 1استادیار، دانشکده مهندسی کامپیوتر، دانشگاه صنعتی شاهرود، شاهرود، ایران | ||
| 2دانشجوی کارشناسی ارشد، دانشکده مهندسی کامپیوتر، دانشگاه صنعتی شاهرود، شاهرود، ایران | ||
| 3استادیار، دانشگاه صنعتی شاهرود، شاهرود، ایران | ||
| تاریخ دریافت: 02 آذر 1399، تاریخ بازنگری: 24 دی 1399، تاریخ پذیرش: 20 دی 1399 | ||
| چکیده | ||
| یکی از مهم‌ترین چالش‌های امنیتیبا پیشرفت فناوری در فضای مجازی حملات فیشینگ یا تله‌گذاری است.تله‌گذاری نوعی حمله سایبری است که همواره در تلاش برای به‌دست آوردن اطلاعاتی مانند نام کاربری، گذرواژه، اطلاعات حساب بانکی و مانند آن‌ها از طریق جعل یک وب‌سایت، آدرس ایمیل و متقاعد کردن کاربر به منظور واردکردن این اطلاعات می‌باشد. با توجه به رشد صعودی این حملات و پیچیده‌ترشدن نوع حمله، سیستم‌های تشخیصتله‌گذاری فعلی اغلب نمی‌توانند خود را با حملات جدید تطبیق دهند و دارای دقت پایین در شناسایی هستند.روشهای مبتنی بر گراف یکی از روشهای شناسایی دامنه‌های مشکوک است که از ارتباطات بین دامنه و IP برای شناسایی استفاده می‌کند. در این مقاله سیستم تشخیص تله‌گذاری مبتنی برگراف با استفاده از یادگیری عمیق ارائه شده است. مراحل کار شامل استخراج IP از دامنه، تعریف ارتباط بین دامنه‌ها، تعیین وزن‌ها وهمچنین تبدیل داده‌ها به بردار توسط الگوریتم Node2vecاست. در ادامه با استفاده از نمونه‌های یادگیری عمیق CNN و DENSE عمل طبقه‌بندی و شناسایی انجام می‌شود. نتایج نشان می‌دهند که روش ارائه شده در این مقالهدقتی در حدود99 درصد در شناسایی دامنه‌های مشکوک دارد که در مقایسه با روشهای قبل بهبود قابل قبول داشته است. | ||
| کلیدواژه‌ها | ||
| تشخیص دامنه مشکوک؛ داده‌های DNS؛ تله‌گذاری؛ یادگیری عمیق | ||
| عنوان مقاله [English] | ||
| Malicious Domain Detection using DNS Records | ||
| نویسندگان [English] | ||
| M. Rezvani1؛ F. Bagheri2؛ Mansoor Fateh3؛ Esmaeel Tahanian1 | ||
| 1Assistant Professor, Faculty of Computer Engineering, Shahroud University of Technology, Shahroud, Iran | ||
| 2Master student, Faculty of Computer Engineering, Shahroud University of Technology, Shahroud, Iran | ||
| 3Assistant Professor, Shahroud University of Technology, Shahroud, Iran | ||
| چکیده [English] | ||
| One of the most important security challenges with the advance of technology in cyberspace is phishing attacks. Phishing is a type of cyber-attack that always tries to obtain information such as username, password, bank account information, and the like by forging a website, email address and convincing the user to enter this information. Due to the increasing growth of these attacks and the increasing complexity of the type of attack, current phishing detection systems often cannot adapt to new attacks and have low detection accuracy. Graph-based methods are one of the techniques for identifying malicious domains that use the connections between the domain and IP to identify. In this paper, a graph-based phishing detection system using deep learning is presented. The main steps in the proposed method include extracting IP from the domain, defining the relationship between the domains, determining the weights, and converting the data to a vector by the Node2vec algorithm. Then, using CNN and DENSE deep learning models, the classification and identification operations are performed. The experimental results over three different datasets show that the proposed method provides an accuracy of about 99% in identifying malicious domains, which has an acceptable improvement compared to state of the art in this context. | ||
| کلیدواژه‌ها [English] | ||
| Malicious domain detection, DNS data, phishing, deep learning | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 762 تعداد دریافت فایل اصل مقاله: 669 |
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