[agents] CFP: SI on NEXT GENERATION SECURITY AND PRIVACY INSPIRING INTELLIGENT AND LEARNING TECHNOLOGIES

[Ismaeel Al Ridhawi]

*CALL FOR PAPERS*

* Special Issue on ‘**NEXT GENERATION SECURITY AND PRIVACY INSPIRING
INTELLIGENT AND LEARNING TECHNOLOGIES**’*

https://wol-prod-cdn.literatumonline.com/pb-assets/assets/24756725/NEXT%20GENERATION%20SECURITY%20AND%20PRIVACY%20INSPIRING%20INTELLIGENT%20AND%20LEARNING%20TECHNOLOGIES%20030619-1559555521247.pdf


Intelligent technologies represent a remarkable transformation for smart
cities. Such technologies have enhanced the quality and performance of
urban and sub-urban services, including transport, energy, healthcare and
traffic. With today’s advancements in Artificial Intelligence (AI),
user-specific service availability and delivery has been made readily
available at reduced costs. As smart cities continue to deploy such
intelligent and learning technologies to improve the performance and
diversity of smart city services, one of the main issues that prevails is
efficient and reliable security and privacy towards both the infrastructure
and users. The hype surrounding 5G and AI is already forcing companies to
quickly upgrade their current solutions, tools, and technology to
accommodate massive data volumes. However, the large-scale deployment of
5G, Cloud, Fog and Mobile Edge Computing (MEC) will bring new challenges;
security and privacy are some examples.

AI and machine learning provide a mechanism to automate the creation of
analytical models in order to enable algorithms to learn continuously. This
is achieved with the aid of the ever-growing smart city data. Continuously
evolving successful models and solutions reduces the need for human
involvement and interaction. These evolved models can be used to produce
reliable and repeatable decisions autonomously. Such models are now being
used in intelligent and autonomously-run smart vehicles. The advent of such
new learning solutions leads to more security, privacy and cryptographic
challenges.

This special issue intends to explore and address the important issues and
challenges related to the security and privacy for intelligent and learning
technologies. The objective is to explore the potentials of machine
learning and big data analytics to provide advanced solutions for
integration within the smart city architecture. We believe that machine
learning and big data analytics will play a vital role in providing
enhanced security for intelligent autonomous smart city solutions and
enables organizations to make crucial changes to their security landscape.
Topics of interest include, but are not limited to, the following:

·        Security and privacy architectures for smart cities.

·        Machine learning and big data analytics architectures for smart
city security and privacy.

·        Data confidentiality and privacy in smart cities.

·        Using fog and MEC architectures to provide secure and reliable
communication.

·        Providing secure and reliable Next Generation Networks.

·        Block chain solutions for next generation security and privacy.

·        Machine learning based security detecting protocols.

·        Secure fog, edge and cloud solutions.

·        Intrusion detection and prevention using intelligent and learning
techniques



*Instructions for Manuscripts*

Submitted articles must describe original research which have not been
published or currently under review by other journals or conferences.
Submissions must be directly sent via the Security and Privacy submission
website at https://mc.manuscriptcentral.com/sphs. Select ‘Special Issue’ as
the manuscript type and then, from the dropdown list, please select
‘Security and Privacy in Smart Communities’. Submissions must conform to
the layout and format guidelines for the journal:
http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2475-6725/homepage/ForAuthors.html



*Important Dates*

Manuscript Due: September 31, 2019

Decision Notification: November 31, 2019

Final Manuscript Due: December 30, 2019

Tentative Publication Date: 3rd Quarter of 2020



 *Guest Editors*

*Dr. Ismaeel Al Ridhawi*, University of Ottawa, Ottawa, ON, Canada.
Ismaeel.AlRidhawi at uottawa.ca

*Dr. Omar Alfandi*, Zayed University, United Arab Emirates and
Georg-August-University Goettingen, Germany. Omar.alfandi at zu.ac.ae

*Prof. **Jalel Ben Othman*, L2S lab CNRS Centrale Supelec, University Paris
Sud and University of Paris 13, France, jbo at l2s.centralesupelec.fr
<jbo at l2s.centralesupelec>

*Dr. Moayad Aloqaily*, Gnowit Inc., Ottawa, ON, Canada. Moayad at gnowit.com
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