Investigating Space-Time Dynamics in Live Memory Forensics Using Hybrid Transformer Approaches

Live memory forensics plays a critical role in digital investigations by analyzing volatile memory to detect system anomalies such as malware and unauthorized process activities. Traditional approaches often fall short in modelling the evolving nature of live memory. This study presents a novel Hybrid Space-Time Transformer Architecture combining Swin Transformer for localized spatial feature extraction and Longformer for capturing long-term temporal dependencies. By integrating windowed and sliding attention mechanisms, the proposed method enables precise detection of anomalies such as malware injection and process hijacking. Evaluated on benchmark datasets, the model achieved an accuracy of 95%, F1-score of 0.94, outperforming conventional deep learning and transformer-based approaches. Our work contributes a scalable, interpretable, and highly accurate model for enhancing live memory forensic workflows.