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روش ترکیبی تشخیص ناهنجاری با استفاده از تشخیص انجمن در گراف و انتخاب ویژگی | |
پدافند الکترونیکی و سایبری | |
مقاله 2، دوره 8، شماره 1 - شماره پیاپی 29، خرداد 1399، صفحه 17-24 اصل مقاله (1.07 M) | |
نوع مقاله: مقاله پژوهشی | |
نویسندگان | |
میثم میرزایی1؛ امین اله مه آبادی* 2 | |
1دانشگاه جامع امام حسین (ع) | |
2دانشگاه شاهد | |
تاریخ دریافت: 14 اسفند 1397، تاریخ بازنگری: 28 فروردین 1398، تاریخ پذیرش: 28 خرداد 1398 | |
چکیده | |
تشخیص ناهنجاری یک موضوع مهم در بسیاری از حوزههای کاربردی شامل امنیت، سلامت و تشخیص نفوذ در شبکههای اجتماعی است. بیشتر روشهای توسعه داده شده، فقط از اطلاعات ساختاری گراف ارتباطی یا اطلاعات محتوایی گرهها برای تشخیص ناهنجاری استفاده میکنند. ساختار یکپارچه بسیاری از شبکهها از قبیل شبکههای اجتماعی این روشها را با محدودیت مواجه ساخته است و باعث توسعه روشهای ترکیبی شده است. در این مقاله، روش ترکیبی پیشنهادی تشخیص ناهنجاری مبتنی بر تشخیص انجمن در گراف و انتخاب ویژگی ارائه شده است که از ناهنجاری بهعنوان اعضای ناسازگار در انجمنها بهره برده و با استفاده از الگوریتم مبتنی بر تشخیص و ترکیب انجمنهای مشابه، شناسایی گرههای ناهنجار را انجام میدهد. نتایج آزمایشهای تجربی روش پیشنهادی بر روی دو مجموعه از دادههای دارای ناهنجاری واقعی، نشاندهنده قدرت تشخیص دقیق گرههای ناهنجار و قابل مقایسه با آخرین روشهای علمی است. | |
کلیدواژهها | |
تشخیص ناهنجاری؛ شبکههای اجتماعی؛ داده کاوی؛ گراف کاوی | |
عنوان مقاله [English] | |
Hybrid Anomaly detection method using community detection in graph and feature selection | |
نویسندگان [English] | |
M. Mirzaee1؛ A. Mahabadi2 | |
1Imam Hossien university | |
2shahed university | |
چکیده [English] | |
Anomaly detection is an important issue in a wide range of applications, such as security, health and intrusion detection in social networks. Most of the developed methods only use graph structural or content information to detect anomalies. Due to the integrated structure of many networks, such as social networks, applying these methods faces limitations and this has led to the development of hybrid methods. In this paper, a proposed hybrid method for anomaly detection is presented based on community detection in graph and feature selection which exploits anomalies as incompatible members in communities and uses an algorithm based on the detection and combination of similar communities. The experimental results of the proposed method on two datasets with real anomalies demonstrate its capability in the detection of anomalous nodes which is comparable to the latest scientific methods. | |
کلیدواژهها [English] | |
Anomaly detection, Social networks, Data mining, Graph mining | |
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