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, behavior 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.
Utilizing 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.
Scope and Topics
This interdisciplinary special session aims at providing a focused discussion forum for utilizing 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 programing, genetic algorithms, particle swarm optimization, 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:
Evolutionary Computation techniques
Data mining in cybersecurity
Evolutionary Transfer learning in cybersecurity
EC techniques for Feature extraction, selection and construction in cybersecurity
White-box and Black-box attacks
Adversarial machine learning
Measurement and ground truth acquisition
Creation of synthesized training and test sets
Learning in games
Automated vulnerability and penetration testing
Ransomware, Spam and phishing detection
Behavioral-based anomaly detection
DDoS prediction and detection
EC methods for Intrusion prevention and response
Keystroke and other biometric dynamics
Vulnerability testing through intelligent probing (e.g. fuzzing)
Privacy preserving data release
Privacy preserving data publishing
Paper Submission Deadline: 7 Jan 2019
Notification of Acceptance: 7 Mar 2019
Final Paper Submission Deadline: 31 Mar 2019
Papers for IEEE CEC 2019 should be submitted electronically through the Congress website at http://www.cec2019.org/papers.html#submission, 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: http://cec2019.org/. All accepted papers will be published in the IEEE CEC 2019 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 Foundation.
Zhen Ni is currently an Assistant Professor in Department of Electrical Engineering and Computer Science (EECS), South Dakota State University (SDSU), Brookings, SD. He received his Ph.D. degree from the Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island (URI), Kingston, RI, in 2015. He received B.S in Department of Control Science and Engineering (currently renamed as College of Automation), Huazhong University of Science and Technology (HUST), Wuhan, China, in 2010.
His research mainly includes Smart Grid, Computational Intelligence, Machine Learning, Adaptive Control, and Cyber-Physical Systems. He is very active in professional societies, including IEEE Computational Intelligence Society (CIS). For instance, he served as the General Chair for IEEE CIS winter school on Computational Intelligence for Big Data, Washington D.C. (2016), and Technical Program Co-Chair for IEEE International Conference on Cyber, Physical, and Social Computing (CPSCom), Halifax, Canada (2018). He also organized a special issue of Cyber-Physical Power Systems on IET Cyber Physical Systems: Theory & Applications (2017-2018). He is an Associate Editor for IEEE Computational Intelligence Magazine (IF: 6.343) from 2018.
He received the Chinese Government Award for Outstanding Students Abroad by Chinese government (2014), Second Prize of Graduate Student Poster Contest in IEEE Power and Energy Society General Meeting (2015), Enhancement of Graduate Research Award (EGRA) by URI (2014), Travel Award by IEEE SSCI-Doctoral Consortium (2014), National Encouragement Scholarship by Ministry of Education in China (2007), and all Outstanding Academic Students awards in HUST (2006-2010).
Wanlei Zhou received the B.Eng and M.Eng degrees from Harbin Institute of Technology, Harbin, China in 1982 and 1984, respectively, and the PhD degree from The Australian National University, Canberra, Australia, in 1991. He also received a DSc degree (a higher Doctorate degree) from Deakin University in 2002. He is currently the Head of School of Software in University of Technology Sydney (UTS). Before joining UTS, Professor Zhou held the positions of Alfred Deakin Professor, Chair of Information Technology, and Associate Dean of Faculty of Science, Engineering and Built Environment, Deakin University. Professor Zhou has been the Head of School of Information Technology twice (Jan 2002-Apr 2006 and Jan 2009-Jan 2015) and Associate Dean of Faculty of Science and Technology in Deakin University (May 2006-Dec 2008). Professor Zhou also served as a lecturer in University of Electronic Science and Technology of China, a system programmer in HP at Massachusetts, USA; a lecturer in Monash University, Melbourne, Australia; and a lecturer in National University of Singapore, Singapore. His research interests include security and privacy, bioinformatics, and e-learning. Professor Zhou has published more than 400 papers in refereed international journals and refereed international conferences proceedings, including many articles in IEEE transactions and journals.
Program Committee (TBC)
Ryan Ko (The University of Waikato, New Zealand)
Fabio Roli (University of Cagliari, Italy)
Giovanni Russolo (The University of Auckland, New Zealand)
Yufei Tang (Florida Atlantic University, USA)
Vijay Varadharajan (The University of Newcastle, Australia)
Bing Xue (Victoria University of Wellington, New Zealand)
Jun Yan (University of Concordia, Canada)
Roland Yap (National University of Singapore, Singapore)
Tianqing Zhu (University of technology, Australia)
Jun Zhang (Swinburne University of Technology, Australia)
Mengjie Zhang (Victoria University of Wellington, New Zealand)