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Subject:
From:
Moayad Aloqaily <[log in to unmask]>
Reply To:
Moayad Aloqaily <[log in to unmask]>
Date:
Wed, 20 Nov 2019 23:22:41 +0400
Content-Type:
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Special Issue Call for Papers: Deep Learning for 5G IoT Systems

http://www.springer.com/cda/content/document/cda_downloaddocument/CFP+JMLC+Special+Issue+-+DL+for+5G+IoT.pdf?SGWID=0-0-45-1661559-p173917603
International Journal of Machine Learning and Cybernetics

https://link.springer.com/journal/13042

Science Citation Index Expanded, Impact Factor  3.844. JCR Q1.


In recent years, deep learning architectures, such as: deep neural
networks, deep belief networks, recurrent neural networks and convolutional
neural networks, have been successfully applied to many fields, including
computer vision, speech recognition, natural language processing, audio
recognition, social network filtering, medical image analysis, material
inspection, where deep learning systems have produced results comparable to
and in some cases superior to human experts. There is an increasing number
of 5G IoT systems, due to the advancement of electronics and communication
techniques (e.g., wearable electronics, IoT devices, and
5G telecommunication solutions). Such technologies have enhanced the
quality and performance of urban and suburban services, including
healthcare, transport, energy, traffic, to name few. In recent years, with
the prevalence of 5G IoT systems, while AI technologies enable
more autonomous and intelligent functions, the security of these systems
has become more and more important as more and more personal data are
generated and communicated through such modern 5G IoT systems. Some of
these emerging security problems cannot be solved by traditional security
measures or by traditional privacy enhancement technologies. As a
result, current 5G IoT system architectures are facing significant
challenges to handle the security
and privacy of increasing number of devices and servers as well as the
protection of large volume of data that is processed in real-time.
Therefore, new security methods and privacy protection solutions which
depend on deep learning are required to build more secure and better
privacy-preserving 5G IoT systems. An increasing trend in integrating deep
learning with access control, intrusion detection/prevention, and behaviour
analysis of 5G IoT systems has been recently observed. Such integration
will play a vital role in providing enhanced security for intelligent
autonomous 5G IoT systems and enables organizations to make crucial changes
to their security landscape.

The focus of this special theme is on emerging deep learning models,
architectures, algorithms and applications in simulating, modelling,
analysing, optimization, and control of emerging 5G IoT systems. Researchers,
developers, and industry experts are welcome to contribute papers for this
special issue. Topics include but are not limited to the followings:
• Emerging deep learning models and applications for 5G IoT systems
• Hybrid deep learning models and applications for 5G IoT systems
• Deep learning architecture/algorithms for large-scale 5G IoT systems
• Deep learning for the prediction of data communications in 5G IoT systems
• Deep learning techniques for intrusion detection/prevention of 5G IoT
systems
• Deep learning-based data analytics and decision automation in 5G IoT
systems
• Deep learning-based malware detection of 5G IoT systems
• Deep learning-based behaviour analysis of 5G IoT systems

Submissions
Submitted articles must describe original research which has not been
published or currently under review by other journals or conferences. All
manuscripts will be peer-reviewed. Instructions for Authors are available
at the website: https://link.springer.com/journal/13042. Authors should
visit the journal website for information on submission. An electronic copy
of the complete manuscript should be submitted ensuring that the paper is
identified as being submitted for this special issue. The special issue has
been created as a submission question. The authors should choose “Original
Paper” as the main article type for their papers and in the upcoming
next submission steps, they will be prompted to answer a question “Does
this manuscript belong to a special issue?”. For the response, a list of
all special issues names will be displayed, and the authors can choose the
special issue. The chosen special issue name will be displayed in
“Details Page” and not under “Article Type” column in the online submission
system. Please direct any questions about this special issue to Xiaochun
Cheng ([log in to unmask]) or Moayad Aloqaily ([log in to unmask]).

Important dates:
• Manuscript submission deadline: 28 August 2020
• First round review notification: 28 October 2020
• Revised manuscript submission deadline: 28 December 2020
• Final decision notification: 28 February 2021
• Expected publication: 28 March 2021

Guest Editors:
• Dr. Xiaochun Cheng, Middlesex University, London, UK
• Dr. Moayad Aloqaily, xAnalytics, Ottawa, Canada
• Prof. Chengqi Zhang, University of Technology, Sydney; Australia
• Prof. Yi Qian, University of Nebraska-Lincoln, USA
• Prof. Yang Xiao, The University of Alabama, USA

*Best Regards*
*Moayad Aloqaily, Ph.D., P.Eng.*

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