دانلود رایگان مقاله انگلیسی یادگیری عمیق به منظور افزایش امنیت شبکه های امنیت تعریف شده با نرم افزار - IEEE 2018

عنوان فارسی
چارچوبی برای یادگیری عمیق به منظور افزایش امنیت شبکه های امنیت تعریف شده با نرم افزار
عنوان انگلیسی
A Deep Learning Framework to Enhance Software Defined Networks Security
صفحات مقاله فارسی
0
صفحات مقاله انگلیسی
6
سال انتشار
2018
نشریه
آی تریپل ای - IEEE
فرمت مقاله انگلیسی
PDF
نوع مقاله
ISI
نوع نگارش
مقالات پژوهشی (تحقیقاتی)
رفرنس
دارد
پایگاه
اسکوپوس
کد محصول
E10550
رشته های مرتبط با این مقاله
مهندسی کامپیوتر، فناوری اطلاعات
گرایش های مرتبط با این مقاله
هوش مصنوعی، امنیت اطلاعات، رایانش امن، شبکه های کامپیوتری
مجله
کنفرانس بین المللی شبکه های اطلاعاتی پیشرفته و کارگاه های کاربردی - International Conference on Advanced Information Networking and Applications Workshops
دانشگاه
School of computing Engineering and Mathematics - Western Sydney University - Sydney - Australia
کلمات کلیدی
شبکه های نرم افزار محور؛ یادگیری عمیق؛ شناسایی ناهنجاری ها؛ اتوکدر
doi یا شناسه دیجیتال
https://doi.org/10.1109/WAINA.2018.00172
۰.۰ (بدون امتیاز)
امتیاز دهید
چکیده

Abstract


Software-Defined Networks (SDN) initiates a novel networking model. SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. The architecture enhances the network resilient, decompose management complexity, and support more straightforward network policies enforcement. However, the model suffers from severe security threats. Specifically, a centralized network controller is a precious target for two reasons. First, the controller is located at a central point between the application and data planes. Second, a controller is software which prone to vulnerabilities, e.g., buffer and stack overflow. Hence, providing security measures is a crucial procedure towards the fully unleash of the new model capabilities. Intrusion detection is an option to enhance the networking security. Several approaches were proposed, for instance, signature-based, and anomaly detection. Anomaly detection is a broad approach deployed by various methods, e.g., machine learning. For many decades intrusion detection solution suffers performance and accuracy deficiencies. This paper revisits network anomalies detection as recent advances in machine learning particularly deep learning proofed success in many areas like computer vision and speech recognition. The study proposes an intrusion detection framework based on unsupervised deep learning algorithms.

نتیجه گیری

CONCLUSION


Deep learning algorithms achieved a breakthrough in neural networks. With a strong record of successful applications, deep learning is a promising approach for network anomalies detection. The paper showed the potential of unsupervised deep learning to enhance the security of SDN. We applied deep autoencoders to calculate a reconstruction error for network traffic records. Then we apply a K-means as clustering algorithm on REs. Our approach showed robust prediction with reasonable training data. However, further research should investigate dimensionality in traffic records, where the number of dimensions is relatively small.


بدون دیدگاه