Chair: Longzhi Yang, Northumbria University, UK. Email:
Vice-Chair: Danda B. Rawat, Howard University, USA (Americas) Email:
Vice Chair: Shui Yu, Deakin University, Australia (Oceania) Email:
Vice-Chair: Nitin Naik, Ministry of Defence, UK (Europe) Email:
Vice-Chair: Basie von Solms, University of Johannesburg, South Africa (Africa)
Vice-Chair: Chunxiao Jiang, Tsinghua University, China (Asia) Email:
Advisor: Yonghong Peng, Sunderland University, UK. Email:
Advisor: Robert H. Deng, Singapore Management University, Singapore Email:
Advisor: Kui Ren, University at Buffalo, USA Email:

Aim, Scope and Objectives

Big data science is the study on collecting, storing, aggregating, governing, archiving, analysing and other ways of manipulating massive volume of data with very high dimensions constantly being generated by many different systems, devices and applications, to solve complex problems analytically. It provides a new opportunity in fighting cyber attacks which are the deliberate attempts from unauthorized parties to access information and communication systems in unlawful means with the goal of theft, disruption and damage. With the help of big data technology, it is expected that the majority of cyber threats can be predicted, detected, responded and prevented at machine speed.


This SIG focuses on big data analytics and analysis in the field of cyber security, which uses advanced data analysis techniques to uncover hidden patterns and unknown correlations in cyber attacks such as malware and spam emails from large and varied data streams that are continuously collected from information and communication systems. Due to the 5Vs of big data (Volume, Velocity, Variety, Veracity, and Value), this SIG is particularly interested in further developments and applications of knowledge discovery, data mining and machine learning algorithms of clustering, classification, prediction, diagnosis, optimisation, and association in dealing with cyber security threats of malware/ransomware, compromised devices, zero-day attacks, and other malicious insiders. The algorithms are typically implemented using mathematical and nature-inspired approaches such as Bayesian networks, artificial neural networks, annealing, deep learning, Bayesian networks, Genetic algorithms, Dempster Shafer’s theory, fuzzy logic, Mont Carlo, parameter estimation, recommendation engines, similarity matching, amongst others, which are all within the range of this SIG.


Another focus of this SIG is big data governance in the field of cybersecurity which transforms raw machine data in “full fidelity” to a ready-to-use big data warehouse to facilitate big data analysis for the time being and future to come. Big data governance is fundamentally a quality control discipline, regarding mainly data veracity, privacy and security, for assessing, managing, using, improving, monitoring, maintaining, and protecting the big data. In the context of cybersecurity, it is about the quality, management and policies of all available data (including metadata) in the information and technology systems for cyber security purposes. The big data governance processes ensure data assets are formally managed, can be trusted and of certain use even if some data are of low quality.


One increasingly important aspect within the realm of big data governance is big data privacy, which often stands out as a separate research topic and thus this is also the interest of this SIG. Given that big data usually are continuously generated by collecting any and every sort of data in the cyber world, big data privacy also can be named as cyber privacy in the ear of big data and thus they are often used interchangeably. Cyber privacy focuses on whether data in the cyber world are protected in compliance with from the existing standards within the organisations to internationally common accepted or even controversial policies. Cyber privacy issues attract the interest from both service users and providers. The information and communication providers should have a certain level of confidence in big data handling to make sure the balance right between big data privacy and the gaols in cyber security, which are often argued from old day definitions including openness, disclosure, secondary usage, correction, and security, to more modern concerns such as source tracking (i.e., lineage), data quality, personal privacy, industry compliance criteria, transparency, and the value to end users amongst others.


This SIG is also interested in the information and communication architecture that enables the collection, aggregation and processing of big data. The infrastructure should be able to deal with massive amounts of data streams constantly being generated by many different computers, communication devices, mobiles, Internet of Things (IoT) and applications. These data streams are then pulled together and managed using data governance processes. The infrastructure may also be expanded to accommodate the data governance processes and data analytics tools, which jointly forms an integrated platform offering a unified place to aggregate security related data and to perform analytics on them, whilst the privacy issues are well addressed.


The main sub-areas of interest include, but not limit to:


  • Big data collection/generation for cybersecurity
  • Big data for privacy
  • The privacy aspect of big data
  • Information integration for cyber security
  • Big data governance for cybersecurity
  • Big data security in the context of cybersecurity
  • Big data algorithms for cyber security
  • Big data analytics for cybersecurity
  • Computer and network infrastructure/architecture for big data collection
  • Platform for cyber threat management using big data technologies
  • Cyber threats detection using big data technologies
  • Fire wall based on big data
  • Security prediction using big data
  • Cyber-attack risk analysis using big data technologies


Founding Members

Shui Yu, Deakin University, Australia
Danada B. Rawat, Howard University, USA
Nitin Naik, Ministry of Defence, UK
Chunxiao Jiang, Tsinghua University, China
Basie von Solms, University of Johannesburg, South Africa
Yonghong Peng, University of Sunderland, UK
Robert H. Deng, Singapore Management University, Singapore
Kui Ren, University at Buffalo, USA
Longzhi Yang, Northumbria University, UK
Jie Li, University of Tsukuba, Japan
Jinsong Wu, Universidad de Chile, Chile
Elize Ehlers, University of Johannesburg, South Africa
Prashant Pillai, University of Wolverhampton, UK
Xin Fu, Xiamen University, China
Graham Sexton, Northumbria University, UK
Fei Chao, Xiamen University, China
Noe Elisa Nnko, University of Dodoma, Tanzania
Salim Amour Diwani, University of Dodoma, Tanzania
Matogora Jabera, Microsoft Innovation Center, Tanzania
Natthakan Iam-On, Mae Fah Luang University, Thailand
Daniel Neagu, Bradford University, UK
Philip Anderson, Northumbria University, UK
Tossapon Boongoen, Thai Air Force Academy, Thailand
Liang Xiao, Xiamen University, China
Kai Meng Tay, University Malaysia Sarawak, Malaysia
Mselle, Leonard, University of Dodoma, Tanzania
Dustin van der Haar, University of Johannesburg, South Africa
Mark Heyink, Information Governance Consultancy, Johannesburg, South Africa
Paul Jenkins, Ministry of Defence, UK
Fanghong Yu, ZET Corporation, China
Yi Cao, Nanjing Customs District, China
Neil Eliot, Northumbria University, UK
Eng Tseng Lau, Queen Mary University of London, UK
Thabiso Peter Mpofu, Harare Institute of Technology, Zimbabwe
Jie Li, Northumbria University, UK
Xiaohua Feng, University of Bedfordshire, UK
Susanrito Susanrito, ST Electronics Info-Software System, Singapore
Zhiyong Zhang, Henan University of Science and Technology, China
Hong Qing Yu, University of Bedfordshire, UK
Oleksandr Lemeshko, Kharkiv National University of Radio Electronics, Ukraine
V. Vijayakumar, VIT University, Chennai, India
Leslie F. Sikos, University of South Australia, Australia
Clive CHEONG TOOK, University of Surrey, UK


Call for Paper

IEEE WCCI 2018 Special Session FUZZ-IEEE-04 on "Fuzzy Logic Systems for Security and Forensics". More details can be found here.

Special Issue "Healthcare System Innovation" in "Applied System Innovation".