کاربرد شبکههای عصبی «حافظه بلندمدت-کوتاهمدت» و «پیچشی» برای شناسایی حملات ممانعت از سرویس توزیعشده | ||
| پژوهش های نظری و کاربردی هوش ماشینی | ||
| مقاله 2، دوره 2، شماره 1، شهریور 1403، صفحه 14-37 اصل مقاله (1.38 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/abmir.2023.19764.1025 | ||
| نویسندگان | ||
| سید مجتبی متین خواه* 1؛ علی خاک باز2؛ فضل اله ادیب نیا3 | ||
| 1استادیار بخش فناوری اطلاعات دانشکده مهندسی کامپیوتر دانشگاه یزد | ||
| 2دانشکده مهندسی کامپیوتر، دانشگاه یزد، یزد، ایران | ||
| 3دانشکده مهندسی کامپیوتر، دانشگاه یزد،یزد، ایران | ||
| چکیده | ||
| یادگیری عمیق به دلیل توانایی در تجزیه و تحلیل الگوهای پیچیده ترافیک شبکه با قابلیت پاسخهای خودکار بلادرنگ، ابزار مهمی برای تشخیص حمله ممانعت از سرویس توزیعشده است. ولی در اینجا مسئله اصلی نوظهور بودن آن است که باعث شده بررسی کامل فرصتها و چالشها در این زمینه با پیادهسازیهای واقعی یا نمونه دادههای محک انجام نشده باشد. در این مقاله دو روش تشخیص حمله ممانعت از سرویس بهوسیله یادگیری عمیق LSTM و CNN و همچنین روش پیشنهادی جدیدی با ترکیب آنها معرفی میشود. نتایج نشان می دهد که هر دو روش LSTM و LSTM-CNN به طور مداوم از نظر درستی، دقت، بازیابی و امتیازات F1 بهتر از CNN عمل می کنند. بررسیهای ما نشان داد که CNN میتواند به طور خودکار ویژگیهایی مانند اندازه بسته، زمان، و آدرسهای منبع/مقصد را از ترافیک خام شبکه یاد بگیرد؛ از سوی دیگر، LSTM بهویژه برای تشخیص الگوهای توالی زمانی حملات در ترافیک شبکه مفید است. از طرف دیگر انتخاب بین استفاده از CNN یا LSTM برای تشخیص DDoS به ویژگیهای خاص مجموعهداده حمله، و اهمیت نسبی ویژگیهای مکانی و زمانی در شناسایی حملات DDoS بستگی دارد. در نهایت، چالشهایی مثل بیشبرازش، پیچیدگی رایانشی، تفسیرپذیری، محدودیتهای داده و حملات خصمانه بررسی میشود و دلیل تردیدها در گزارش نتایج مقالات میتواند به مشکلات مجموعهداده محک مورد استفاده مانند عدم کیفیت نمونهها بر اساس اندازه و تنوع محدود، عدم برچسبگذاری، دادههای نامتعادل، نسبت داده شود. | ||
| کلیدواژهها | ||
| یادگیری ماشین؛ یادگیری عمیق؛ CNN؛ DDoS؛ LSTM | ||
| عنوان مقاله [English] | ||
| Using Long Short-Term Memory and Convolutional neural networks to Detect Distributed Denial of Service Attacks | ||
| نویسندگان [English] | ||
| S.Mojtaba Matinkhah1؛ Ali Khakbaz2؛ Fazlolah Adibnia3 | ||
| 1Computer Eng. Department, Yazd University, Yazd, Iran | ||
| 2Computer Engineering, Yazd University, Yazd, Iran | ||
| 3Computer Eng. Department, Yazd University, Yazd, Iran | ||
| چکیده [English] | ||
| Deep learning is an essential tool for detecting distributed denial of service (DDoS) attacks due to its ability to analyze complex network traffic patterns and respond in real-time. However, a comprehensive examination of the opportunities and challenges in this field is necessary, given its emerging nature. This examination should include real implementations or benchmark data samples. In this paper, we introduce two methods for detecting DDoS attacks: one using Long Short-Term Memory (LSTM) and the other using Convolutional Neural Networks (CNN). Additionally, we propose a new method that combines LSTM and CNN. The results demonstrate that both LSTM and LSTM-CNN methods consistently outperform CNN in terms of accuracy, precision, recovery, and F1 scores. Our investigations reveal that CNN can automatically learn features such as packet size, timing, and source/destination addresses from raw network traffic. On the other hand, LSTM is particularly useful for detecting temporal sequence patterns of attacks in network traffic. The choice between CNN or LSTM for DDoS detection depends on the specific characteristics of the attack dataset and the relative importance of spatial and temporal features in identifying DDoS attacks. Finally, we examine challenges such as overfitting, computational complexity, interpretability, data limitations, and hostile attacks. Doubts surrounding the reporting of results in literature can be attributed to problems with the benchmark dataset used, including limited sample size and variety, lack of labeling, and unbalanced data. | ||
| کلیدواژهها [English] | ||
| CNN, DDoS, Deep Learning, Machine Learning, LSTM | ||
| مراجع | ||
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