Moon Cloud’s Post

Our Bayesian Multivariate Time-series Graph Neural Network model (B-MTGNN) forecasts the gap between the trend of cyber-attacks and pertinent mitigation technologies 3 years in advance. Published in: Technological Forecasting and Social Change (Top 1% by Scopus, 2024). I would like to express my sincere gratitude to Professor Paul Y. for his outstanding guidance and novel ideas throughout this research. I would also like to extend my thanks and appreciation to my co-authors, Professor Ernesto Damiani and Professor Raymond Choo. Highlights: • Seminal prediction of cyber-attack trends vs. mitigation technology gaps. • Proactive machine learning boosts cyber threat prediction for strategic planning. • Data-centric forecasting reduces bias and subjectivity in cyber threat analysis. • Bayesian GNN predicts cyber threat-technology gaps 3 years in advance. • Alleviation Tech Cycle model for optimised cyber security resource allocation. Paper Link: https://lnkd.in/dgUtVxff