Keynote Speech Information - Victor CHANG
- Professor of Business Analytics at Aston Business School, Aston University, UK; previously Professor of Data Science and Information Systems at Teesside University, UK
- Expertise in AI-oriented Data Science, with contributions across multiple disciplines; involved in projects worth over £14 million in Europe and Asia, leading more than four major projects valued at over £3 million
- Numerous awards, including IEEE Outstanding Young Scientist (2017), Highly Cited Researcher (2021), Top 2% Scientist (2019-2024), and shortlisted for Inspirational Individual of the Year 2024, UK National IT Award
Biography: Victor Chang is a Professor of Business Analytics at Operations and
Information Management, Aston Business School, Aston University UK, since mid-May 2022. He was
previously a Professor of Data Science and Information Systems at the School of Computing,
Engineering and Digital Technologies, Teesside University, UK. He has deep knowledge and
extensive experience in AI-oriented Data Science and has significant contributions in multiple
disciplines. Within 4 years, Prof Chang completed Ph.D. (CS, Southampton) and PGCert (Higher
Education, Fellow, Greenwich) while working for several projects simultaneously. Before
becoming an academic, he has achieved 97% on average in 27 IT certifications. He won 2001 full
Scholarship, a European Award on Cloud Migration in 2011, IEEE Outstanding Service Award in
2015, best papers in 2012, 2015 and 2018, the 2016 European award: Best Project in Research,
2016-2018 SEID Excellent Scholar, Suzhou, China, IEEE Outstanding Young Scientist award in
2017, IEEE 2017 special award on Data Science, 2017-2023 INSTICC Service Awards, Talent Award
Suzhou 2019, Top 2% Scientist between 2019 and 2024, top Business Research Scholar, the most
productive AI-based Data Analytics Scientist between 2010 and 2019, Highly Cited Researcher
2021, Top 125 British Computing Scientists 2022-2024 and numerous awards mainly since 2011.
Prof Chang was involved in different projects worth more than £14 million in Europe and Asia.
He has led more than 4 major projects worth more than £3 million. He has published 3 books as
sole authors and the editor of 2 books on Cloud Computing and related technologies. He
published 1 book on web development, 1 book on mobile app and 1 book on Neo4j. He gave 54
keynotes at international conferences. He is widely regarded as one of the most active and
influential young scientists and experts in IoT/Data Science/Cloud/security/AI/IS, as he has
the experience to develop 10 different services for multiple disciplines. He is the founding
conference chair for IoTBDS, COMPLEXIS and FEMIB to build up and foster active research
communities globally with positive impacts and has recently stepped down. He has won the
Inspirational Individual of the Year 2024, UK National IT Award.
Title: Innovations in Network Security with AI: Cross-chain IoT Transactions, Fraud
Detection, and AI-based Malware Protection
Abstract:
Securing networks and identifying malicious activity have become critical concerns in our
hyperconnected society. This keynote offers and explains an integrated framework for network
security advances that integrates fraud detection, AI-driven malware prevention, and
cross-chain IoT transactions, aiming to improve the resilience of digital ecosystems.
A proposed architecture that tackles the end-to-end security of networked systems forms the foundation of this research. In order to guarantee equity, transparency, and data integrity, the first component focuses on protecting cross-chain IoT data transfers by utilizing cutting-edge cryptographic protocols and blockchain-inspired technology. By enabling safe and fair data sharing across many networks, this method serves as the cornerstone of a reliable IoT data marketplace.
Building on this framework, the second element uses cutting-edge fraud detection methods to improve the security of digital payment systems. This study reduces the hazards of unbalanced datasets and offers strong fraud detection capabilities by utilizing machine learning models and data balancing techniques like the Synthetic Minority Over-sampling Technique (SMOTE). This element successfully detects fraudulent activity, guaranteeing the dependability of financial transactions.
The third part deals with malware detection for Network Intrusion Detection Systems (NIDS) with AI-driven federated learning. Federated learning improves intrusion detection's resilience and flexibility by allowing models to be trained across dispersed devices while protecting user privacy. The suggested architecture offers proactive protection against new threats and is well-suited for 5G and 6G mobile IoT scenarios. It also achieves high accuracy in identifying network anomalies.
These three elements work together to provide a thorough approach to network security by combining proactive malware protection, strong fraud detection, and fairness in data exchanges. This lecture will highlight the significance of an integrated AI-driven approach to protecting interconnected systems and give participants insights into how these breakthroughs collectively contribute to a more secure and resilient digital future.
A proposed architecture that tackles the end-to-end security of networked systems forms the foundation of this research. In order to guarantee equity, transparency, and data integrity, the first component focuses on protecting cross-chain IoT data transfers by utilizing cutting-edge cryptographic protocols and blockchain-inspired technology. By enabling safe and fair data sharing across many networks, this method serves as the cornerstone of a reliable IoT data marketplace.
Building on this framework, the second element uses cutting-edge fraud detection methods to improve the security of digital payment systems. This study reduces the hazards of unbalanced datasets and offers strong fraud detection capabilities by utilizing machine learning models and data balancing techniques like the Synthetic Minority Over-sampling Technique (SMOTE). This element successfully detects fraudulent activity, guaranteeing the dependability of financial transactions.
The third part deals with malware detection for Network Intrusion Detection Systems (NIDS) with AI-driven federated learning. Federated learning improves intrusion detection's resilience and flexibility by allowing models to be trained across dispersed devices while protecting user privacy. The suggested architecture offers proactive protection against new threats and is well-suited for 5G and 6G mobile IoT scenarios. It also achieves high accuracy in identifying network anomalies.
These three elements work together to provide a thorough approach to network security by combining proactive malware protection, strong fraud detection, and fairness in data exchanges. This lecture will highlight the significance of an integrated AI-driven approach to protecting interconnected systems and give participants insights into how these breakthroughs collectively contribute to a more secure and resilient digital future.