تشخیص نفوذ در ابر رایانشی توسط تکنیک تشخیص ناهمگونی
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمندعلی غفاری 1 , روزبه حسین نژاد 2
1 - دانشیار دانشگاه آزاد اسلامی واحد تبریز، گروه مهندسی کامپیوتر، تبریز، ایران
2 - دانشگاه آزاد اسلامی واحد تبریز، گروه مهندسی کامپیوتر، تبریز، ایران
کلید واژه: ابر رایانشی, رفتار مغرضانه, رفتار نرمال, تشخیص ناهنجاری, پارامترهای رفتاری,
چکیده مقاله :
در سال‌های اخیر، ظهور و گسترش روزافزون استفاده از رایانش ابری، درک همه از معماری‌های زیرساخت، ارائه نرم‌افزار و مدل‌های توسعه را به شدت تغییر داده است. پس از انتقال از رایانه‌های مرکزی به مدل‌های سرویس گیرنده/سرویس دهنده، رایانش ابری عناصری از محاسبات گرید، محاسبات ابزار و محاسبات خودکار را در بر می‌گیرد و به یک معماری استقرار نوآورانه تبدیل می‌شود. این انتقال سریع به سمت رایانش ابری، نگرانی‌هایی برای موفقیت سیستمهای اطلاعاتی، ارتباطات و امنیت اطلاعات ایجاد کرده است. ساختار توزیع شده رایانش ابری به عنوان هدفی جذاب برای حملات سایبری بالقوه توسط نفوذگران تبدیل می شود. در این مقاله با استفاده از رویکرد تشخیص ناهنجاری، روشی برای تعبیه یک سیستم تشخیص نفوذ برای رایانش ابری ارائه شده است. لذا با مطالعه بر روی چگونگی بررسی پارامترها و نقش ترکیبی پارامترها در تشخیص نفوذ در ابر، به بررسی و ارائه چهارچوب نظری به همراه شبیه‌سازی رفتار مشکوک در ابر پرداخته شده است. منطقی‌ترین روش برای شناسایی نفوذ، استفاده از روش‌های همراه با ناظر برای یادگیری پارامترهای رفتار عادی مشتریان است. لذا تشخیص رفتار مغرضانه در قالب رفتار مشکوک، با یک شبیه‌سازی اولیه در قالب شناسایی رفتار غیرعادی در حیطه‌های مختلف رفتاری توسط شبکه عصبی تکرار‌کننده پیاده‌سازی شده و مورد بحث، بررسی و مقایسه قرار گرفت. نتایج حاصل از شبیه‌سازی روش پیشنهادی نشان می‌دهد که این تحقیق می‌تواند جنبه‌های جدیدی را برای بررسی مسئله تشخیص نفوذ در ابر ارائه کند و از روش دسته‌بندی نیز کارایی بهتری از خود نشان دهد.
Introduction: The distributed structure of cloud computing makes it an attractive target for potential cyberattacks by intruders. In this paper, using the anomaly detection approach, a method for embedding an intrusion detection system for cloud computing is presented. Therefore, by studying how to check the parameters and the combined role of the parameters in the detection of penetration in the cloud, a method for detecting suspicious behavior in the cloud is provided. The most logical way to detect an intrusion is to use supervised methods to learn the parameters of normal customer behavior. Therefore, the detection of biased behavior in the form of suspicious behavior was implemented and discussed, investigated, and compared with an initial simulation in the form of identifying abnormal behavior in different behavioral areas by the neural network.Method: In this article, the basis of abnormality detection in different aspects is to examine the behavior of users and use the capabilities of reproducing inputs in RNN neural networks. In these networks, during the training of the network, the weights are adjusted in such a way that they can minimize the average square of the error so that the network can produce common repeating patterns well. Therefore, after training, these networks cannot reproduce well the input patterns that are actually significantly different from the training samples. Hence, these networks are able to identify anomalies in the tested sets. Accordingly, RNN networks are used here to model normal behavior.Findings: The simulation results show that the proposed method, which is based on the recurrent neural network, can improve the false positive, false negative, and detection accuracy compared to the classification method.Discussion: In this article, the detection of biased behavior in the form of suspicious behavior was implemented and discussed, investigated, and compared with an initial simulation in the form of identifying abnormal behavior in different behavioral fields. The simulation results show that the proposed method, which is based on the iterative neural network, can improve the false positive, false negative, and detection accuracy compared to the classification method.
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