The Research and Postgraduate Centre congratulates Asst. Prof. Dr. Li Jing from the School of Computing and Data Science, for his recent research article published in IEEE Transactions on Network and Service Management, an SCI Q1 journal with the latest impact factor of 5.4.
The growing demands of Industry 5.0 necessitate resilient Internet of Things (IoT) networks, which are increasingly susceptible to sophisticated cyber threats. While advancements in intrusion detection systems (IDS) have improved attack detection, addressing the complexity of multi-class attack scenarios and managing minority threats remains challenging.
In the paper entitled “Adaptive NetFlow IIoT Intrusion Detection with Deep Transfer Learning, Genetic Optimization, and Ensemble Methods for Network Management”, the researchers propose NFIIoT-DTL-IDS, an adaptive IoT IDS for smart network management using NetFlow and IIoT data, driven by deep transfer learning and enhanced with genetic algorithm (GA) optimization. By combining deep learning with evolutionary optimization, this research contributes a scalable and high-performance solution for intelligent IoT network management and intrusion detection, aligning with the growing security demands of Industry 5.0.
The research was conducted in collaboration with Dr. Mohd Shahizan Othman from the Universiti Teknologi Malaysia, and Dr. Dina S. M. Hassan from Mansoura University. This work is financially supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project (PNURSP2025R751).
Dr. Li Jing is an Assistant Professor at the School of Computing and Data Science, Xiamen University Malaysia. His research interests include machine learning, deep learning, computer networking, the Internet of Things (IoT), cybersecurity, digital twins, big data, high-performance computing, and applications of large language models.