Big Data Analytics and Deep Learning for Social Network Security

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The world is becoming smarter with the advancement in technology for data collection, storage and maintenance, in addition to artificial intelligence and machine learning techniques. Consequently, data and the concern to measure and judge its capabilities, in the current business and technology era, are indispensable for competitive advancement. According to the International Institute for Analytics, businesses which uses data will accomplish close to USD 430 billion in the year 2020. Indeed, data is becoming a vital asset for gaining insights that can help in making strategic decisions for a secured future of any business. Especially, big data platforms and the associated data science domain are substantial to make enormous understanding and help to create new growth opportunities and categories that can combine and analyze rapidly growing industrial data. Network analysis may play a considerable role in the new setup. However, security becomes a major issue when it comes to big data and network analysis in a distributed environment. Therefore, big data analysts, network security experts, and data scientists hold a prominent position in the current era, where data scientists are highly needed and there is a visible shortage in the market.

Almost every technology giant uses Big Data for informative and comprehensive decision making. Consequently, the analytics applied should be more accurate for future success. This may be highlighted as one of the main reasons behind applying Deep Learning and network analysis algorithms for Big Data analytics to achieve unprecedented levels of accuracy. Further, intelligent computing can be enhanced with Big Data and network analytics to enable and interpret extensive unstructured real-world data and intelligently react to associated events. This special issue highlights the challenges and solutions of Deep Learning and Network Security algorithms for Big Data, to improve security effectiveness for data.

SUBJECT COVERAGE

Topics include, but are not limited to, the following:

  • Behavioral and technological perspectives
  • Convolutional Neural Network for productive network computing
  • Auto encoders, Boltzmann machine, Deep Belief Network architectures for the deep analytics
  • Innovative methods for big data analytics, Techniques for mining unstructured, spatial-temporal, streaming and multimedia data, Machine learning from big data, Search and optimization for big data networks
  • Parallel accelerated and distributed big data analytics, Value, and performance of big data and network analytics
  • Data and network visualization, Real-world applications of big data and network analytics, such as default detection, cybercrime, e-commerce, e-health, etc., Improving forecasting models using big data analytics
  • Security and privacy in the big data and networking era, Online community and big data, analytics and decision support, Data mining and knowledge discovery, Web data mining (web content mining, web usage mining)
  • Network analysis in social communities, Intelligent computing on data analytics
  • Unsupervised learning and network analysis procedures to handle massive, unstructured data
  • Intelligent language processing for the universal conversion, Machine translation
  • Social Security for e-commerce in business applications networks
  • Search algorithms on social networks and Data mining
  • Security and privacy in social networks and Information clusters
  • Social media monitoring and analysis using deep learning
  • Spatio-temporal aspects of social networks and social media using unstructured data

(All topics related to Big Data/Deep Learning to enhance Social Network Security will be considered)


IMPORTANT DATES

Submission of manuscripts: 05 February 2021

Notification to Authors: 10 May 2021

Final versions due: 25 September 2021


SUBMISSION INSTRUCTIONS  

Articles reporting original  and  unpublished research  results  pertaining to  the above topics are solicited. Submitted  articles will  follow  an academic review  process. Manuscripts  must be prepared  according to  the  instruction for authors  available  at the  journal  webpage and  submitted  through the publisher's online submission system, available at: https://www.editorialmanager.com/snam

Please note: when submitting choose the correct special issue, i.e. “S.I: Big Data - Social Network Security” 


GUEST EDITORS

Prof. Dr. B.Nagaraj     (Lead Guest Editor)
Dean - Innovation Centre, Rathinam Group of Institutions, Coimbatore, Tamilnadu, India
dean.sa@rathinam.in

Prof. Dr. Danilo Pelusi,
University of Teramo, Dept. of Communication Engineering, Italy
dpelusi@unite.it

Prof. Dr. Valentina E. Balas
Professor-Automation and Applied Informatics, Aurel Vlaicu University of Arad, Romania
valentina.balas@uav.ro