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Chen. J Surveill Secur Saf  2020;1:x                         Journal of Surveillance,
               DOI: 10.20517/jsss.2020.26                                        Security and Safety




               Editorial                                                                     Open Access


               Welcome to the Journal of Surveillance, Security
               and Safety: A New Open-Access Scientific Journal



               Xiaofeng Chen

               School of Cyber Engineering, Xidian University, Xi’an 710126, Shaanxi, China.
               Correspondence to: Prof. Xiaofeng Chen, School of Cyber Engineering, Xidian University, Xi’an 710126, Shaanxi, China.
               E-mail: xfchen@xidian.edu.cn

               How to cite this article: Chen X. Welcome to the Journal of Surveillance, Security and Safety: A New Open-Access Scientific
               Journal. J Surveill Secur Saf 2020;1:102-5. http://dx.doi.org/10.20517/jsss.2020.26

               Received: 23 Sep 2020    Accepted: 23 Sep 2020    Published: xx Sep 2020

               Academic Editor: Xiaofeng Chen    Copy Editor: Cai-Hong Wang    Production Editor: Jing Yu



               INTRODUCTION
               This journal has been anxiously awaited by those interested in the security and safety problems associated
               with artificial intelligence, the blockchain, databases, cloud computing, multimedia, wireless networks, IoT,
               and other computer science and cryptography technologies.


               In recent years, security threats faced by new technologies are emerging without end, while the security
               requirements of traditional technologies are increasing. Interest in these areas has grown rapidly, mainly
               including the security issues from the perspectives of AI, data, network, computing, cryptography, access
               control, industries, policies, models, etc. The deeper is the awareness of private data that people have, the
               higher is their need for application security.

               Explosive growth in the number and scale of machine learning models, requiring robustness in their
               training and testing periods against adversarial attacks, is one of the most striking characteristics of the
               current technological landscape about artificial intelligence. The expansion of research related to both
               machine learning-based attacks and their interpretation has driven the rapid growth of the research area
               in secure machine learning models. The urgent need for security research is not a unique trend in a certain
               field, and tricky challenges regarding security issues also appear in other popular areas, e.g., the blockchain,
               where multiple system components, such as consensus mechanism and smart contract, have been found
               susceptible to malicious attacks that destroy the credibility of the system.




                           © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0
                           International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
                sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long
                as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,
                and indicate if changes were made.


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