مرور سیستماتیک روش ‌های یادگیری عمیق برای تشخیص حملات پیشرفته در شبکه ‌های تبادل داده

چکیده مقاله :

با گسترش روزافزون اینترنت اشیا، کلان داده‌ ها و تبادلات لحظه ای اطلاعات در بستر شبکه های ارتباطی، مسئله امنیت اطلاعات به یکی از چالش های اساسی نظام های ارتباطی مدرن تبدیل شده است. ظهور تهدیداتی نظیر نفوذ های چند مرحله ای، حملات روز صفر و حملات پایدار پیشرفته (APT)، کارآمدی بسیاری از سامانه های سنتی تشخیص نفوذ را با تردید مواجه ساخته است. طی سال های اخیر، به کارگیری مدل‌های یادگیری عمیق به‌ عنوان جایگزینی مؤثر برای روش های کلاسیک، به‌ ویژه به دلیل توانایی این مدل ها در شناسایی الگوهای پیچیده، مورد توجه ویژه قرار گرفته است. این پژوهش با هدف ارتقای علمی و روزآمدسازی مطالعات پیشین، به بررسی انتقادی و تحلیلی مجموعه ای از روش های مبتنی بر معماری‌ های AE، DNN، CNN، LSTM، GRU و ترکیبی از آنها در زمینه شناسایی تهدیدات پیچیده در شبکه های تبادل داده می پردازد. ضمن تحلیل تجربی عملکرد این مدل‌ ها در محیط های واقعی و مقایسه نظام مند نتایج حاصل، چالش های اجرایی و محدودیت های پیاده سازی نیز واکاوی شده اند. همچنین، با تکیه بر منابع معتبر و بروز (منتشر شده بین سال های 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 سامانه های مقاوم تر در برابر تهدیدات نوظهور در معماری‌ های شبکه های ارتباطی آینده باشد..

چکیده انگلیسی :

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 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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