Although the rapid growth in technology and the Internet has simplified many different tasks in our daily life, this reliance on the Internet also makes us vulnerable to new types of security threats. Cybersecurity aims at preventing and detecting cyber attacks on Internet-connected systems which include data, software, and hardware, in order to maintain the confidentiality, integrity, and availability of those assets. On one hand, the diversity of attacks on such assets, which vary in nature, behaviour and methodology makes the task of detecting such attacks more difficult. On the other hand, the limitation of having enough labelled data makes the task even harder to build a good model for researchers wanting to apply computational intelligence techniques. The lack of data makes transfer learning a promising paradigm where data from related (source) domains can be utilized to tackle the problem in the target domain to effectively increase the size of the labelled data sets.
Utilising various evolutionary computation (EC) and machine learning (ML) techniques to tackle numerous problems related to cybersecurity have received increasing attention due to the success of such techniques to tackle problems in many other domains.
This interdisciplinary special session aims at providing a focused discussion forum for utilising EC based techniques to automatically tackle different cybersecurity-related problems such as intrusion prevention and detection, malware
detection, spam and phishing filtering, and other types of network-based attacks, e.g., DDoS (distributed denial of service). It also aims at promoting both practical applications and theoretical development of EC, e.g., genetic
programming, evolutionary programming, genetic algorithms, particle swarm optimisation, artificial immune systems, learning classifier systems, techniques for information and network security domains.
The scope of this special session covers, but not limited to, the following topics:
Papers for IEEE CEC 2021 should be submitted electronically through the Congress website at http://cec2021.mini.pw.edu.pl, and will be refereed by experts in the fields and ranked based on the criteria of originality, significance, quality and clarity. To submit your papers to the special session, please select the Special Session name in the Main Research topic.
For more submission information please visit: https://cec2021.mini.pw.edu.pl/en. All accepted papers will be published in the IEEE CEC 2021 electronic proceedings, included in the IEEE Xplore digital library, and indexed by EI Compendex.
Harith Al-Sahaf received the B.Sc. degree in computer science from Baghdad University (Iraq), in 2005. He joined the Victoria University of Wellington (VUW), (New Zealand) in July 2007 where he received his MCompSc and PhD degrees in Computer Science in 2010 and 2017, respectively. In October 2016, he has joined the School of Engineering and Computer Science, VUW as a Post-doctoral Research Fellow and as a full-time lecturer since September 2018. His current research interests include evolutionary computation, particularly genetic programming, computer vision, pattern recognition, evolutionary cybersecurity, machine learning, feature manipulation including feature detection, selection, extraction and construction, transfer learning, domain adaptation, one-shot learning, and image understanding. He is a member of the IEEE CIS ETTC Task Force on Evolutionary Computer Vision and Image Processing, the IEEE CIS ETTC Task Force on Evolutionary Computation for Feature Selection and Construction, the IEEE CIS ISATC Task Force on Evolutionary Deep Learning and Applications, and the IEEE CIS ISATC Intelligent Systems for Cybersecurity.
Ian Welch has a PhD from the University of Newcastle upon Tyne. His current research includes machine learning for network security, IoT-specific security policies and
honeypots. Prior to becoming an academic, he worked for a range of employers including the State Services Commission, Deloitte Touche Tohmatsu Limited, Accenture and the UK National Health System. He is a board member of the Faucet
Zhen Ni received the B.S. degree in control science and engineering from Huazhong University of Science and Technology, Wuhan, China, in 2010, and the Ph.D. degree
in electrical, computer and biomedical engineering from the University of Rhode Island, Kingston, RI, USA, in 2015. He is currently an Assistant Professor with the Department of Computer, Electrical Engineering, and Computer Science,
Florida Atlantic University, Boca Raton, FL, USA. He was with the Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USA, from 2015 to 2019. His current research interests include
computational intelligence, reinforcement learning, and smart grid applications.
Prof. Ni is a recipient of the prestigious IEEE Computational Intelligence Society Outstanding Ph.D. Dissertation Award (2020), INNS Aharon Katzir Young Investigator Award (2019), the URI Excellence in Doctoral Research Award (2016), and the Chinese Government Award for Outstanding Students Abroad (2014). He has been actively involved in numerous conference and workshop organization committees in the society, including the General Co-Chair of the IEEE CIS Winter School, Washington, DC, USA, in 2016. He is an Associate Editor of the IEEE Computational Intelligence Magazine (2018-), an Associate Editor of the IEEE Transactions of Neural Networks and Learning Systems (2019-), and a Guest Editor for IET Cyber-Physical Systems: Theory and Applications (2017-2018).
Paul S. Pang is an Associate Professor of cyber security at the School of Engineering, Information Technology and Physical Sciences, Federation University Australia.
Before joining Federation University, he was a Professor of Data Analytics and Director of Center Computational Intelligence for Cybersecurity at the Unitec Institute of Technology, New Zealand.
Dr. Pang has acted as a Principle Investigator for over 13 research grant projects, totalling more than NZD$3.5 million in funding by the Ministry of Business, Employment and Innovation, NZ (MBIE), the Ministry for Primary Industries, NZ (MPI), the Health Research Council, NZ (HRC), the National Institute of Information and Communications Technology, Japan (NICT), Telecom NZ, Mitsubishi Electric Japan, LuojiaDeyi Technology China, and Lucent & Bell Lab USA.
His main research areas are Cognitive Cyber Security Intelligence, Cyber Resilience, and Applied Data Analytics for Digital Health. He published over 100-refereed articles with international journals and conferences including IEEE TPDS, TSMC-B, TKDE, TNN, Neural Networks, Pattern Recognition, IEEE Cloud, IoT, and IEEE/ACM UCC. He had so far 1 patent and filed 3 patent applications with 1 sold to a NZ company.
Dr. Pang has received many awards of distinction including IEEE ICNNSP Best Paper Award (2003), IEEE DMAI Best Paper Award (2008), Unitec FCBI Research Excellency Award (2011-2012), Unitec FCBI Executive Dean Award (2012), Unitec Award for Meeting the Needs of Communities (2012), Unitec FCBI Research Excellency Award (2014), and Unitec Research with Impact Award (2015).
Dr. Pang is a Senior Member of IEEE, the Event Editor of Neural Network Journal Elsevier, the Vice President of Asia Pacific Neural Network Society (APNNS), a Global Judge for the 2018 AI summit London, and an Australian Research Council (ARC) Assessor for National Competitive Grant Program.