مقاله انگلیسی رایگان در مورد چارچوب پیش بینی حملات و شکست ها برای یک شبکه تلفن همراه 5G مشترک – اسپرینگر 2021

 

مشخصات مقاله
ترجمه عنوان مقاله چارچوب پیش بینی حملات و شکست ها برای یک شبکه تلفن همراه 5G مشترک
عنوان انگلیسی مقاله Attacks and failures prediction framework for a collaborative 5G mobile network
انتشار مقاله سال 2021
تعداد صفحات مقاله انگلیسی  17 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه اسپرینگر
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) scopus – master journals – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
2.500 در سال 2020
شاخص H_index 54 در سال 2021
شاخص SJR 0.556 در سال 2020
شناسه ISSN 0010-485X
شاخص Quartile (چارک) Q2 در سال 2020
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس ندارد
رشته های مرتبط مهندسی فناوری اطلاعات، فناوری اطلاعات و ارتباطات
گرایش های مرتبط دیتا و امنیت شبکه، شبکه های کامپیوتری، اینترنت و شبکه های گسترده، مخابرات سیار، سامانه های شبکه ای
نوع ارائه مقاله
ژورنال
مجله  محاسبات – Computing
دانشگاه scopus – master journals – JCR
کلمات کلیدی امنیت سایبری، مدیریت خطا، تشخیص ناهنجاری، شبکه های تلفن همراه، مدیریت شناختی، یادگیری ماشین، یادگیری اجرای مجدد
کلمات کلیدی انگلیسی Cyber-security – Fault management – Anomaly detection – Mobile networks – Cognitive manegement – Machine learning – Re-enforcement learning
شناسه دیجیتال – doi
https://doi.org/10.1007/s00607-020-00893-8
کد محصول  E15257
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
1 Introduction
2 Related works
3 Attacks and failures prediction framework
3.1 Features engineering module
3.2 Prediction module
3.3 Actuation module
4 AFPA for resilient 5G network slices
4.1 Deployment and initial training phase
4.2 Run-time phase
4.3 AFPA for a multi-technology network
5 Numerical experimentation
5.1 ARIMA model for attacks prediction
5.2 Failures prediction ML model
5.3 Anomaly prediction ML model
6 Conclusion
References

 

بخشی از متن مقاله:

Abstract

Although mobile technologies keep evolving through years, Fault management and cyber-security management in mobile networks are still treated as separated notions with different blocks and different approaches whereas in practice, they are highly correlated. In this paper, we propose a framework that takes into account the correlation between these two management systems. The framework is based on several prediction agents where each agent is composed of a security predictor, a fault predictor and a generic anomaly detection model. A re-enforcement process allows to enhance the reliability of the machine learning training and prediction phases of the different predictors. Besides, each agent can collaborate with its neighborhood for a more resilient network. An application of this framework to 5G architecture is proposed by mapping the components of our framework with network slices. Finally, an experimentation is held over a testbed that we set up on openstack in order to forecast future anomalies related to proxy overload, latency violation in call session network functions and to excessive usage of memory. The training is achieved with ARIMA and deep learning models with promising results.

1 Introduction

As mobile network technologies evolve, new services are offered and more sophisticated networks are needed. The increasing number of Internet users leads to a redesign of network architecture, forcing designers to take into account new parameters such as the need of global coverage combined with low latency, as well as a high reliability and security level. Additionally, new networking experiences are added, such as Internetof-Things (IoT), which promise to offer new services and facilities to people’s daily lives. In this demanding environment, 5G technology is emerging, playing a decisive role in the implementation of new visions and promising to deliver solutions. A major innovation introduced by 5G technology [1] is the scalability. 5G architectures take into account the possible need of extending the capabilities of the network, both at the level of user traffic growth and at the level of new services input from providers. Slicing could be the ideal solution for such networks, offering scalability as well as flexibility in managing a giant network. Network Slicing is set to be a prominent feature of 5G to allow connectivity and data processing tailored to specific customers requirements. Mobile communications provided by smart networks will enhance the efficiency and productivity of business processes and will open up opportunities for network operators to address the Business-to-Business segment more effectively.

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