Comparative analysis of novel gradient boosting algorithm and recurrent neural network algorithms for malware detection and classification.

This study aimed to find a better way to identify and categorize malware. Researchers compared two algorithms: a Novel Gradient Boosting Algorithm and a Recurrent Neural Network (RNN) Algorithm. They used a larger dataset of malware samples (10,000 entries with 35 details about each). To ensure a reliable comparison, they carefully selected a portion of the data (80%) for training and set a high standard for accuracy (95% confidence level). The results were clear: the Novel Gradient Boosting Algorithm significantly outperformed the other one. It achieved an impressive 91.7% accuracy in identifying malware, whereas the Recurrent Neural Network only managed 79.5%. This difference was statistically proven using a special test (p-value less than 0.005). In conclusion, this study suggests that the Novel Gradient Boosting Algorithm is a much stronger tool for malware detection and classification compared to Recurrent Neural Networks. The researchers recommend using this method in future systems designed to fight malware. [ABSTRACT FROM AUTHOR]