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| تعداد شماره‌ها | 1,240 |
| تعداد مقالات | 8,994 |
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مدل ترکیبی تشخیص ناهنجاری با استفاده از خوشه بندی وزنی معکوس و یادگیری ماشین در بستر محیطهای ابری | ||
| پدافند الکترونیکی و سایبری | ||
| مقاله 2، دوره 9، شماره 4 - شماره پیاپی 36، اسفند 1400، صفحه 21-29 اصل مقاله (987.88 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| عادله جعفر قلی بیک1؛ محمد ابراهیم شیری احمد آبادی* 2؛ افشین رضاخانی3 | ||
| 1دانشجوی دکتری، گروه کامپیوتر، دانشگاه آزاد اسلامی واحد بروجرد، بروجرد، ایران | ||
| 2استادیار،گروه کامپیوتر،دانشگاه صنعتی امیرکبیر، تهران، ایران | ||
| 3استادیار،گروه کامپیوتر،دانشگاه آیت الله بروجردی،بروجرد، ایران | ||
| تاریخ دریافت: 07 آبان 1399، تاریخ بازنگری: 24 مرداد 1400، تاریخ پذیرش: 24 مرداد 1400 | ||
| چکیده | ||
| امروزه به دلیل حملات و نفوذهای بسیار پیشرفته، شناسایی حملات در اینترنت اشیاء در بستر محیطهای ابری بسیار دشوار شده است. از مشکلات دیگر سیستمهای ابری میتوان به پایین بودن دقت در تشخیص خطا، نرخ مثبت کاذب و زمان محاسبات طولانی اشاره کرد. در روش پیشنهادی یک مدل تشخیص نفوذ ترکیبی شامل یک الگوریتم خوشهبندی و یک طبقهبندی جنگل تصادفی مبتنی بر ماشین، برای محیطهای ترکیبی مه و ابر ارائه میدهیم. همچنین برای کنترل ترافیک شبکه در لایه فیزیکی و همچنین تشخیص ناهنجاری در بین دستگاههای اینترنت اشیاء محاسبات در مه و لبههای ابر انجام خواهد شد به این صورت که پس از پیش پردازش، ترافیک ورودی به مه و ابر بررسی و در صورت نیاز به یک ماژول تشخیص ناهنجاری هدایت میشوند. برای شناسایی نوع هر حمله از یک طبقهبندی یادگیری مبتنی بر جنگل تصادفی استفاده شده است. از دادههای عمومی و دادههای ابری برای تحقیق استفاده شده است. دقت کلی سیستم تشخیص نفوذ پیشنهادی 03/98 و متوسط نرخ مثبت کاذب 17 % و نرخ تشخیص ناهنجاری 30/96 بوده است که نسبت به روشهای گذشته قابل ملاحظه است. | ||
| کلیدواژه‌ها | ||
| سیستم تشخیص نفوذ؛ محاسبات ابری؛ محاسبات مه؛ تشخیص ناهنجاری؛ اینترنت اشیاء | ||
| عنوان مقاله [English] | ||
| The Presentation of a Hybrid Anomaly Detection Model Using Inverse Weight Clustering and Machine Learning in Cloud Environments | ||
| نویسندگان [English] | ||
| Adeleh jafar gholi beik1؛ M. E. Shiri Ahmad Abadi2؛ Reza Rezakhani3 | ||
| 1PhD student, computer department, Islamic Azad University, Borujard branch, Borujard, Iran | ||
| 2Assistant Professor, Computer Department, Amir Kabir University of Technology, Tehran, Iran | ||
| 3Assistant Professor, Computer Department, Ayatollah Borujerdi University, Borujerd, Iran | ||
| چکیده [English] | ||
| Today, due to highly advanced attacks and intrusions, it has become very difficult to detect IoT attacks in cloud environments. Other problems with cloud systems include low error detection accuracy, false positive rates, and long computation times. In the proposed method, we present a hybrid intrusion detection model including a clustering algorithm and a machine-based random forest classification for the fog and cloud environments. Also, to control the network traffic in the physical layer and also to detect the anomalies between IoT devices, calculations are performed on the fog and the edges of the cloud, so that after preprocessing, the incoming traffic to the fog and cloud are checked and if necessary, they are directed to an anomaly detection module. A random forest-based learning classification is used to identify the type of each attack. Both the general and cloud data have been used for this research. The overall accuracy, the mean false positive rate and the anomaly detection rate of the proposed intrusion detection system are 98.03, 17% and 96.30 respectively, which is notable in comparison to previous methods . | ||
| کلیدواژه‌ها [English] | ||
| IDS, Cloud Computing, Fog Computing, Anomaly Detection, IoT | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 514 تعداد دریافت فایل اصل مقاله: 461 |
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