مقاله انگلیسی رایگان در مورد چارچوبی برای یادگیری عمیق به منظور افزایش امنیت شبکه – ۲۰۱۸ IEEE

IEEE

 

مشخصات مقاله
ترجمه عنوان مقاله چارچوبی برای یادگیری عمیق به منظور افزایش امنیت شبکه های امنیت تعریف شده با نرم افزار
عنوان انگلیسی مقاله A Deep Learning Framework to Enhance Software Defined Networks Security
انتشار مقاله سال ۲۰۱۸
تعداد صفحات مقاله انگلیسی ۶ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه IEEE
مقاله بیس این مقاله بیس نمیباشد
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی کامپیوتر، فناوری اطلاعات
گرایش های مرتبط هوش مصنوعی، امنیت اطلاعات، رایانش امن، شبکه های کامپیوتری
نوع ارائه مقاله
کنفرانس
کنفرانس 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
کلمات کلیدی انگلیسی  Software-defined networks، Deep Learning، Anomalies Detection، Autoencoders
شناسه دیجیتال – doi
https://doi.org/10.1109/WAINA.2018.00172
کد محصول  E10550
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract

I- Introduction

II- Software-Defined Networks and Related Work

III- Autoencoders

IV- Detection Framework

V- Conclusion

References

 

بخشی از متن مقاله:

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.

Introduction

The conventional communication networking model consists of three planes. i.e., management, control, and forward or data. The management plane supports network monitoring and configuration. The control plane populates forwarding tables on the physical devices. Consecutively, the forward plane switches packets to ingress and egress ports based on the forwarding tables. For decades, both the Control and the forward planes are integrated into the same networking devices, for instance. Switches or routers. The conventional model provided efficiency from a performance perspective. However, current networks became excessively complicated, and there is a necessity to adopt a more resilient architecture [1]. This paper introduces a framework to enhance the security deficiencies of SDN.The framework is anomalies detection based on machine learning. The next section discusses SDN model and related security threats. The third section investigates the deep learning and its current anomalies detection solution for network security. The fourth section represents our proposed framework.

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