دانلود مقاله امنیت رایانش ابری و یادگیری عمیق
خلاصه
1. معرفی
2. ادغام رویکرد یادگیری عمیق برای امنیت ابر
3. شبکه عصبی و مدل پیشنهادی
4. شبیه سازی و نتایج
5. نتیجه گیری
منابع
Abstract
1. Introduction
2. Integrating Deep Learning approach to cloud security
3. Neural Network and the proposed model
4. Simulation and Results
5. Conclusion
References
چکیده
تکنیک‌های یادگیری عمیق با استفاده از مدل‌های شبکه‌های عصبی مصنوعی تأثیر قابل‌توجهی در افزایش امنیت در حوزه‌های مختلف نشان داده‌اند. هنگامی که برای امنیت رایانش ابری اعمال می شود، یادگیری عمیق راه حل های مقرون به صرفه ای را با خودکارسازی تشخیص تهدید، کاهش نظارت دستی و بهبود اثربخشی کلی امنیت ارائه می دهد. مدل‌های یادگیری عمیق با استفاده از شبکه‌های عصبی نقش مهمی در کارهایی مانند تشخیص نفوذ، تشخیص بدافزار، تشخیص ناهنجاری و تجزیه و تحلیل گزارش دارند. ادغام یادگیری عمیق در امنیت ابری مستلزم ارزیابی دقیق سیستم‌های موجود، تعریف اهداف، انتخاب و آماده‌سازی مجموعه داده، تنظیم مدل و تغییرات نهایی برای سازگاری است. علاوه بر این، پیاده‌سازی تکنیک‌های یادگیری عمیق در امنیت ابری مستلزم در نظر گرفتن عواملی مانند منابع محاسباتی، هزینه‌های جمع‌آوری و آماده‌سازی داده‌ها، توسعه مدل، تلاش‌های یکپارچه‌سازی و نظارت و نگهداری مداوم است. این مقاله یک مدل شبکه عصبی مصنوعی (ANN) انتشار پیش‌خور را در امنیت ابری پیشنهاد می‌کند و مراحل کلیدی را برای ادغام چنین مدل‌هایی در استراتژی‌های امنیت ابری بررسی می‌کند. با توجه به اینکه اثربخشی مدل ANN به عواملی مانند کیفیت داده های آموزشی، معماری شبکه و الگوریتم های تنظیم وزن بستگی دارد، این مطالعه از مجموعه داده ای از Kaggle.com برای اعتبار سنجی استفاده می کند و مراحل مربوط به آموزش و ارزیابی مدل ANN را نشان می دهد.
Abstract
Deep learning techniques have shown significant impact in enhancing security across various domains by leveraging artificial neural networks models. When applied to cloud computing security, deep learning offers cost-effective solutions by automating threat detection, reducing manual monitoring, and improving overall security effectiveness. Deep learning models using neural networks play crucial role in tasks like intrusion detection, malware detection, anomaly detection, and log analysis. Integration of deep learning into cloud security requires careful evaluation of existing systems, defining objectives, dataset selection and preparation, model tuning, and eventual modifications for compatibility. Furthermore, implementing deep learning techniques in cloud security entails considering factors such as computational resources, data collection and preparation costs, model development, integration efforts, and ongoing monitoring and maintenance. This paper proposes a feed-forward propagation Artificial Neural Network (ANN) model in cloud security and investigates the key steps for integrating such models into cloud security strategies. Considering that the effectiveness of the ANN model depends on factors such as training data quality, network architecture, and weight adjustment algorithms, the study utilizes a dataset from Kaggle.com for validation and demonstrates steps involved in training and evaluation of the ANN model.
Introduction
Deep learning (DL) techniques have emerged to become effective tools for enhancing security in various domains. These techniques leverage the capabilities of artificial neural networks to learn and identify patterns from vast amounts of data, enabling more robust and efficient security solutions [1][2]. When applied to cloud computing security, deep learning techniques offer several benefits having direct implications in the cost-effectiveness of the products. A great benefit of implementation of DL in security of cloud is seen in automated threat detection. DL algorithms can analyze large volumes of data, such as network traffic logs, system logs, and user behavior, to detect anomalies and potential security threats automatically [2], [3]. This together with other utilizations as a result of DL, reduces the need for manual monitoring and analysis, what leads to faster identification of security incidents, timely response, mitigation, and reduced costs in time and resources. Considering this, through automation of various security tasks, DL minimizes the risk of human error, which can lead to security breaches and associated costs.
Automated systems powered by deep learning can perform tasks consistently and accurately, enhancing overall security effectiveness [4], [5]. DL models learn patterns and relationships in data, allowing for more accurate threat detection and classification. Through detecting patterns and anomalies which could go unnoticed by traditional rulebased systems, DL implementation makes it possible for organizations to optimize resource allocation and minimize unnecessary costs.
Conclusion
The integration of deep learning techniques into cloud computing security offers numerous benefits. By analyzing large volumes of data, deep learning algorithms can detect security threats in real-time and minimize various risks. However, the implementation of deep learning in cloud security requires careful evaluation of costs and resources. Organizations struggle when having to adjust the solution to the computational resources, data acquisition and preparation costs, personnel expertise, and ongoing monitoring and maintenance.
The integration of deep learning techniques in cloud security involves few steps for a successful outcome. These include assessing the strengths and weaknesses of the existing system, identifying specific tasks that deep learning can address, selecting suitable models and algorithms, training the models with labeled data, and ensuring compatibility and integration with existing components. Continuous monitoring and feedback are also crucial stages for maintaining the efficiency and effectiveness of the integrated system.