Prof. Maode Ma (IET Fellow)

Qatar University, Katar

Prof. Maode Ma, a Fellow of IET, received his Ph.D. from the Department of Computer Science at the Hong Kong University of Science and Technology in 1999. Prof. Ma is a Full Professor in the Faculty of Computer Science and Artificial Intelligence at Shenzhen University of Advanced Technology. Before joining SUAT, he had been a faculty member at Nanyang Technological University and Qatar University for over 25 years. He has extensive research interests in network security, AI security, and wireless networking. He has about 550 international academic publications, which include more than 280 journal papers. His publication has received close to 13,000 citations in Google Scholar. Prof. Ma currently serves as the Editor-in-Chief of the Journal of Communication and Network Security, the International Journal of Computer and Communication Engineering, and the Journal of Communications. He also serves as a Senior Editor for IEEE Communications Surveys and Tutorials, and an Associate Editor for the International Journal of Communication Systems. Prof. Ma is a senior member of the IEEE Communication Society. Prof. Ma has been a Distinguished Lecturer for the IEEE Communication Society from 2013 to 2016 and from 2023 to 2024.

Title: Design of An Automatic Incremental Lifetime Learning IDS 

Abstract: Traditional Intrusion Detection Systems(IDSs) provide limited defense against emerging threats, as they rely on static rules or machine learning (ML) models that lack the capacity for real-time updates. The Incremental Lifetime Learning IDS (ILL-IDS) is a new type of IDS to address this limitation by enabling adaptive learning of new attack types. However, ILL-IDS depends heavily on large volumes of high-quality labeled data, making the model update process costly and labor-intensive. In this talk, the Automatic Incremental Lifetime Learning IDS (AILL-IDS) is intraduced, which is a novel IDS framework that can significantly reduce the need for labelling data by incremental semi-supervised learning. This approach not only enables AILL-IDS to detect unknown types of attacks and adapt its model dynamically with minimal labeled data but also ensures continuous detection during the model update process, enhancing both speed and accuracy in threat detectioninvehicularnetworks or Internet of Thing (IoT) systems. Experimental results demonstrate that AILL-IDS can achieve a high detection rate of 0.97 and an average F1 score of 0.90, labelling only 5.5% of the total traning data, thereby offering an efficient and scalable solution for securing IoT against emerging cyber threats.

Dr Syed Abdul Rehman Khan

Xuzhou University of Technology, China

Dr. Syed Abdul Rehman Khan is an expert in supply chain and logistics management. He achieved his CSCP—Certified Supply Chain Professional certificate in the U.S.A. and completed his postdoctoral fellowship at Tsinghua University.

Dr. Khan is a professor of operations and supply chain management. He has more than twelve years of core experience in supply chain and logistics, both in industry and at the academic level. He has attended several international conferences and has also been invited as a keynote speaker in different countries. He has published more than 200 scientific research papers in various well-renowned international peer-reviewed journals (SSCI/SCI and ABS listed) and conferences, including several research papers that have been indexed in Essential Science Indicators (ESI). He is also the author of four books and the editor of nine books related to sustainability in supply chain and business operations. He is a regular contributor to conferences and workshops around the world.

Title:Blockchain Technology and Circular Economy Practices: A New Era Business Strategies for Environmental Sustainability

Abstract:Amid rising environmental concerns, Industry 4.0 and Blockchain technology (BCT) are transforming circular economy (CE) practices and prevailing business models. Recognize the same; this study examines the role of blockchain technology in circular CE practices; and their impact on eco-environmental performance, which influences organizational performance. The study collects data from 404 enterprises located in Chinese and Pakistani territories, involved in cross-border supply chain operations. Both countries’ sample has great relevance due to the China Pakistan Economic Corridor (CPEC), which possess several positive fallouts in terms of technology spillovers across firms. Using the PLS-SEM modeling framework, this study provides three key findings. First, BCT significantly improves the circular economy practices (circular procurement, circular design, recycling, and re-manufacturing). Second, CE practices help improve firms’ environmental performance and stimulate their financial performance. Third, higher eco-environmental performance significantly boosts organizational performance. This study set-out the foundations for participating countries/firms that simultaneously achieve financial and sustainable goals by integrating blockchain technology in circular economy practices.