Latest research in Dynamic Malware Analysis focuses on leveraging machine learning and advanced neural architectures to improve the detection, classification, and understanding of malware behavior. Studies utilize techniques such as system call frequency analysis combined with algorithms like XGBoost, LightGBM, and Random Forest, achieving high accuracy and precision in malware identification. Innovative models, including self-attention Transformer-based architectures like Nebula, process diverse behavioral data from dynamic analysis logs and demonstrate superior performance with reduced training data requirements. Additionally, approaches addressing imbalanced datasets, such as dynamic classifier selection, enhance detection of minority malware classes, particularly in Android applications. These advancements collectively underscore the growing impact of intelligent, data-driven methodologies in transforming dynamic malware analysis for more efficient and accurate cybersecurity solutions.