Securing IoT-based Critical Infrastructure (VSI-cei)

Overview

Critical Energy Infrastructure (CEI) refers to specific engineering information about proposed or existing critical infrastructure. Modern infrastructures are increasingly moving to distributed and complex cyber-physical systems which requires proactive protection and fast restoration to mitigate physical or cyber attacks, espically, combined physical-cyber attacks, that are much more challenging task and it is expected to become the most intrusive attack. This case is particularly true for the Critical Energy Infrastructures (CEI), e.g., the US Industrial Control Systems Cyber Emergency Response Team responded to 245 plus incidents during 2015, and 32% of these incidents were from the Energy sector.

Considering the importance of energy in our life and its impact on other critical infrastructures, CEI requires significant attention comparatively. Machine learning allows the data to remain on-premise in the infrastructure and used to provide a robust defense mechanism for critical infrastructures. For example, wind-turbine system is considered one of the most complex cyber-physical infrastructures causing huge cascading effects to other critical energy infrastructures, such as transportation, healthcare sector, communications, industry finance and electrical power systems. Such threats to infrastructure enable the responsible authorities to consider the advantages of machine learning, IoT and simultaneously protecting their privacy i.e. there is always a possibility of attacks against these infrastructures which can be predicted and detected efficiently.

This special section aims to stimulate discussion on the design, use and evaluation of machine learning models for Critical Energy Infrastructure towards the improvement of the privacy and security. We invite theoretical work and review articles on practical use-cases of Federated Learning in CEI that discuss adding a layer of trust to powerful algorithms for delivering near real-time intelligence.

Topics:

This special section will respond to the research challenges by encouraging researchers in the computing world to bring to bear novel techniques, combinations of tools, and so forth to build effective ways to Enhancing the Security of Critical Energy Infrastructures. We solicit papers covering various topics of interest that include:

• Securing Critical Infrastructure
• Federated Learning for Critical Infrastructure
• Data privacy solutions for critical infrastructure
• Automated Protection to CEI
• Security and privacy of big data in Energy
• Enhancing the Security of CEI
• Cyber Attacks on C EI
• Machine Learning in Energy Sector
• Model and Infrastructure for Federated Learning in Energy
• Advances and Open Problems in Critical Infra Structure
• Scalable Federated in Energy sector
• Securing Federated learning
• Federated Learning for Crisis in Critical Infrastructure
• Management of Cloud-based critical infrastructure
• Deep learning for Industrial control systems

Submission Guidelines:

New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special issue.

Before submission, authors should carefully read the Guide for Authors available at

https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors

Authors should submit their papers through the journal’s web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-cei” under the “Issues” tab.

For additional questions, contact the Main Guest Editor.

Schedule:

Submission of manuscript: May. 15, 2021

First notification: July 30, 2020

Submission of revised manuscript: August 28, 2021

Notification of the re-review: October 28, 2021

Final notification: November 16, 2020

Final paper due: December 1, 2021

Publication: March. 2022

Guest Editors:

Imran Razzak (Main Guest Editor)

Senior Lecturer,

School of Information Technology,

Deakin University, Geelong,

Australia, 3220

imran.razzak@deakin.edu.au

Imran Razzak is a Senior Lecturer of Computer Science in the School of Information Technology at Deakin University, Geelong, Australia. He has been actively involved in teaching and research since 2010. Previously, he worked at King Saud bin Abdulaziz University for Health Sciences, University of Technology, Malaysia, Air University, Islamabad, and University of Technology, Sydney. He is also visiting fellow/visiting professor at several international universities such as UTS and UTM. He did his PhD in machine learning and data science from University of Technology, Sydney, Master’s in machine learning and Image processing from International Islamic University. He is also heading a research group “RULE” and active collaborating/member of several research groups in different university nationally (UTM, Macquarie University) and internationally such as (DFKI Germany, IIT India, UTM Malaysia, UET Lahore, Universite de Bourgogne, Dublin Institute of Technology etc). Razzak has published more than 100 papers in reputed (mostly in A*/A) journals and conferences. He is author of one book and inventor of one patent on Face Recognition. He has attracted research grant of 2.4 million SAR and has successfully delivered several research projects. He is received numerous research awards (Young Researcher 2015 and Researcher 2017). His area of interest includes machine learning with its application spans a broad range of topics.

Guandong Xu

Professor,

School of Computer Science,

University of Technology, Sydney

Australia, 2007

guandong.xu@uts.edu.au

Guandong Xu is a Professor in the School of Computer Science and Advanced Analytics Institute at University of Technology in Sydney, Australia. He received MSc and BSc degree in Computer Science and Engineering, and PhD in Computer Science. He has succeeded in three academic promotions from Lecturer to full Professor from 2012 to 2018. His research has received funding from Australian Research Council Discovery and Linkage Project, Cooperative Research Centre Program, government and industry, totalling over $5.5M in past years. He currently heads the Data Science and Machine Intelligence Research Lab, which consists of 15+ members of academics, research fellows and HDR students. From Nov 2019, he directs the newly established Smart Future Research Centre, which is an across-disciplines industry engagement and innovation platform for AI and Data Science Applications towards smart wealth and investment management, energy, food, water, living, and city. Guandong has had 200+ publications in the areas of Data Analytics and Data Science, Web Mining, Recommender Systems, Text Mining, Social Computing and Predictive Analytics, including monograph books, edited conference proceedings and dozens of journal and conference papers in top venues.

Ibrahim A Hameed

Professor,

Deputy Head of Research and Innovation

Norwegian University of Science and Technology,

Larsgårdsvegen 2, Ålesund Norway, 6009

ibib@ntnu.no

IBRAHIM A. HAMEED (Senior Member, IEEE) is a Professor at the Department of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science, and Technology (NTNU), Norway. Hameed is Deputy Head of research and innovation within the same department. Hameed is an IEEE senior member and elected chair of the IEEE Computational Intelligence Society (CIS) Norway section. Hameed has a Ph.D. degree in Industrial Systems and Information Engineering from Korea University, Seoul, South Korea and a PhD degree in Mechanical Engineering from Aarhus University, Aarhus, Denmark. His current research interest includes Artificial Intelligence, Machine Learning, Optimization, and Robotics.

Peter Eklund

Professor,

School of Information Technology,

Deakin University, Melbourne,

Australia, 3220

peter.eklund@deakin.edu.au

Peter Eklund has been a Teaching and Research academic since 1992 when he joined the University of Adelaide as a Senior Lecturer in Computer Science after a Postdoc at Hosei University in Tokyo, following his PhD at Linköping University in Sweden. In 1998, he was appointed Foundation Chair of Information Technology at Griffith University. He was Professor of Computer Science at Wollongong from 2004-2013 and before joining Deakin in 2018 was Head of School at the IT University of Copenhagen (2013-2018). Eklund has held three ARC Linkage grants, and two ARC discovery grants, also CI on ARC Research Network and ARC Key Centre programs. He was a key researcher (theme leader) in the DSTC CRC. CI (Eklund) currently supervises 4 PhDs, one co-funded by the Minerals Council of Australia and another co-funded by GAIA Technology. CI (Eklund) has completed 18 PhDs, many of whom have gone to industry, two to Google, four to Amazon, one to FaceBook, one to Groupon, two others founded their own tech start-ups. Further, CI (Eklund) has himself been involved with multiple start-ups.