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تشخیص برخط و استوار ناهنجاری با استفاده از شبکه عصبی بازگشتی | ||
| مدل سازی در مهندسی | ||
| دوره 21، شماره 74، آبان 1402، صفحه 243-254 اصل مقاله (1.16 M) | ||
| نوع مقاله: مقاله کامپیوتر | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2023.29201.2370 | ||
| نویسندگان | ||
| مریم آموزگار* 1؛ مرتضی فائزی نیا2؛ بهروز مینایی بیدگلی3 | ||
| 1استادیار، گروه کامپیوتر و فناوری اطلاعات، پژوهشگاه علوم و تکنولوژی پیشرفته و علوم محیطی، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته | ||
| 2دانشکده مهندسی کامپیوتر، دانشگاه علم و صنعت ایران | ||
| 3دانشیار، دانشکده مهندسی کامپیوتر، دانشگاه علم و صنعت ایران | ||
| تاریخ دریافت: 16 آذر 1401، تاریخ بازنگری: 19 فروردین 1402، تاریخ پذیرش: 20 اردیبهشت 1402 | ||
| چکیده | ||
| در بسیاری از مسائل دنیای واقعی، داده، پویا و دارای نویز است. در چنین شرایطی تشخیص ناهنجاری باید با یک مدل برخطی که در مقابل نویز استوار است، انجام شود. در سالهای اخیر شبکههای عصبی بازگشتی بر روی توالی دادهها مورداستفاده قرار گرفته و نتایج خوبی در این حوزه بدست آوردهاند. اما راهکارهای موجود، استواری کافی در مقابل نویز ندارند. این مقاله، به ارائه راهکاری برای تشخیص ناهنجاری در داده گرافی پویا با استفاده از شبکههای عصبی بازگشتی میپردازد که در مقابل نویز استوار بوده و با تغییرات دادهها تطبیقپذیری کافی را دارند. نسخه استوار ارائه شده از شبکه عصبی بازگشتی، به هدف مدیریت نویز، همزمان با یادگیری الگوی اصلی و تطبیق با تغییرات، ناهنجاریها را استخراج و معرفی میکند. برای بررسی صحت ﻋﻤﻠﮑﺮد روش پیشنهادی، آزمایشهایی ارائه شده که قدرت ﺗﺸﺨﯿﺺ ﻧﺎﻫﻨﺠﺎری و توان ﺗﻄﺒﯿﻖ یادگیرنده را در مقایسه با راهکارهای موجود میسنجد. نتایج، برتری روش پیشنهادی را تصدیق کرده اﺳت. | ||
| کلیدواژهها | ||
| تشخیص ناهنجاری؛ مدل استوار؛ داده پویا؛ شبکه عصبی بازگشتی | ||
| عنوان مقاله [English] | ||
| Online and Robust Anomaly Detection using Recurrent Neural Network | ||
| نویسندگان [English] | ||
| Maryam Amoozegar1؛ Morteza Faezinia2؛ Behrouz Minaei_Bidgoli3 | ||
| 1computer and information technology,, | ||
| 2Department of Computer engineering, Iran University of Science and Technology | ||
| 3Department of Computer engineering, Iran University of Science and Technology, Tehran, Iran | ||
| چکیده [English] | ||
| In many real-world applications, data is dynamic and noisy. In such a situation, anomaly detection should be performed with an online and robust model against noise. In recent years, recurrent neural networks have been used on data sequences and have achieved good performance in this field. The existing methods do not have sufficient robustness against noise. This paper presents a method for anomaly detection in dynamic graph data using recurrent neural networks that are robust against noise and have sufficient adaptivity to changes in the data pattern. The proposed robust recurrent neural network extracts and introduces anomalies for the purpose of noise management. At the same time, it learns the original patterns in an online manner and is adapted to the changes. To evaluate the proposed method, some experiments are presented that measure its ability in anomaly detection in addition to the learning and adaptation ability in comparison with the existing methods. The results have confirmed the superiority of the proposed method. | ||
| کلیدواژهها [English] | ||
| Anomaly detection, Robust model, Dynamic data, Recurrent neural network | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 515 تعداد دریافت فایل اصل مقاله: 340 |
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