A Structured State Space Sequence Model for Multi-Class Classification of Malware
This research introduced a novel Structured State Space Sequence (S4) model for detecting and classifying malware from sequential samples. The S4 model captures long-range dependencies in features better than existing deep learning methods like CNNs and Transformers. It successfully achieved high performance, reaching an 89% macro F1-score for family classification, outperforming baselines by significant margins.