Mr. Naveed Anjum | Security | Best Researcher Award
University of Science and Technology Beijing | China
Mr. Naveed Anjum Mian is a dedicated PhD researcher and Research Fellow with a strong passion for innovative, cost-effective, and realistic approaches in computer science. His research focuses on large language models (LLMs) for social network cyberbullying detection and graph-based network security, leveraging expertise in machine learning, deep learning, and graph neural networks (GNNs). Mr. Mian has extensive teaching experience as a lecturer in computer science, delivering engaging lectures and hands-on lab sessions in programming, database systems, network communication, and object-oriented programming while promoting research and development in network security and AI-driven solutions. His professional experience also includes roles as a software engineer, contributing to web development, user requirement analysis, and the maintenance of large-scale portals like Zameen.com. Academically, he holds an MS in Computer Science with a strong CGPA and a BS in Computer Science, currently pursuing a PhD at the University of Science and Technology Beijing. Mr. Mian is proficient in Python and its libraries, including Transformers, PyTorch, TensorFlow, Keras, Pandas, NumPy, Scikit-Learn, Matplotlib, and Deep Graph, and is skilled in Linux and Hadoop for large-scale data processing. His research contributions include publications on the security and privacy of industrial big data and multi-source data fusion schemes for intrusion detection in networks, accumulating 35 citations with an h-index of 2 and an i10-index of 1. Through his work, Mr. Mian combines analytical rigor with practical applications, contributing to advancements in AI, cybersecurity, and data-driven solutions while fostering knowledge sharing and innovative practices in academia and industry.
Profile: Google Scholar | Oricid
Featured Publications
Anjum, N., Latif, Z., & Chen, H. (2025). Security and privacy of industrial big data: Motivation, opportunities, and challenges. Journal of Network and Computer Applications.
Anjum, N., Latif, Z., Lee, C., Shoukat, I. A., & Iqbal, U. (2021). MIND: A multi-source data fusion scheme for intrusion detection in networks. Sensors, 21(144941).