مرور سیستماتیک روش های یادگیری عمیق برای تشخیص حملات پیشرفته در شبکه های تبادل داده
محورهای موضوعی : فناوری اطلاعات و ارتباطاتمسعود سیاوشی 1 , محمدمهدی شیرمحمدی 2
1 - دانشگاه آزاد اسلامی، واحد همدان
2 - استادیار, گروه مهندسی کامپیوتر، دانشگاه ازاد اسلامی، همدان، ایران
کلید واژه: تهدیدات پایدار پیشرفته (APT), تشخیص نفوذ مبتنی بر یادگیری عمیق, امنیت سایبری در شبکههای ارتباطی, مدلهای ترکیبی CNN-LSTM, چالشهای امنیتی اینترنت اشیا, حملات روز صفر,
چکیده مقاله :
با گسترش روزافزون اینترنت اشیا، کلاندادهها و تبادلات لحظهای اطلاعات در بستر شبکههای ارتباطی، مسئله امنیت اطلاعات به یکی از چالشهای اساسی نظامهای ارتباطی مدرن تبدیل شده است. ظهور تهدیداتی نظیر نفوذهای چندمرحلهای، حملات روز صفر و حملات پایدار پیشرفته (APT)، کارآمدی بسیاری از سامانههای سنتی تشخیص نفوذ را با تردید مواجه ساخته است. طی سالهای اخیر، بهکارگیری مدلهای یادگیری عمیق بهعنوان جایگزینی مؤثر برای روشهای کلاسیک، بهویژه به دلیل توانایی این مدلها در شناسایی الگوهای پیچیده، موردتوجه ویژه قرار گرفته است. این پژوهش با هدف ارتقای علمی و روزآمدسازی مطالعات پیشین، به بررسی انتقادی و تحلیلی مجموعهای از روشهای مبتنی بر معماریهای AE، DNN، CNN، LSTM، GRU و ترکیبی از آنها در زمینه شناسایی تهدیدات پیچیده در شبکههای تبادل داده میپردازد. ضمن تحلیل تجربی عملکرد این مدلها در محیطهای واقعی و مقایسه نظاممند نتایج حاصل، چالشهای اجرایی و محدودیتهای پیادهسازی نیز واکاوی شدهاند. همچنین، باتکیهبر منابع معتبر و بروز (منتشر شده بین سالهای 2020 تا 2025) و باز طراحی گرافیکی جداول و نمودارها، مقاله حاضر تلاش دارد چارچوبی تحلیلی، کاربردی و جامع را در زمینه تشخیص هوشمند حملات در فضای سایبری ارائه دهد. این مقاله میتواند مبنایی علمی برای توسعه سامانههای مقاومتر در برابر تهدیدات نوظهور در معماریهای شبکههای ارتباطی آینده باشد.
Information security has become a fundamental challenge for modern communication systems with the ever-increasing proliferation of the Internet of Things, big data, and real-time information exchanges within communication networks. The emergence of threats such as multi-stage intrusions, zero-day attacks, and Advanced Persistent Threats (APTs) has cast doubt on the effectiveness of many traditional intrusion detection systems. In recent years, the application of deep learning (DL) models has garnered significant attention as an effective alternative to classic methods, particularly due to their ability to identify complex patterns. This research aims to advance scientific knowledge and update previous studies by critically and analytically reviewing a collection of methods based on AE, DNN, CNN, LSTM, GRU, and hybrid architectures for detecting complex threats, with a specific focus on the multi-stage and stealthy nature of Advanced Persistent Threats (APTs) in data exchange networks. While analyzing the empirical performance of these models in real-world environments and systematically comparing the obtained results, implementation challenges and limitations have also been explored. Furthermore, drawing upon credible and up-to-date sources (published between 2020 and 2025) and redesigning tables and diagrams graphically, the present article strives to offer an analytical, practical, and comprehensive framework for intelligent attack detection in cyberspace. This paper can serve as a scientific basis for developing more resilient systems against emerging threats in future communication network architectures.
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