Malware Detection Using Random Forest Algorithm

This project presents a machine learning model for malware detection. The model is trained on a dataset of malware and benign files using the Random Forest algorithm.The model achieves an accuracy of 99% on the test dataset, which means that it can correctly identify malware with a high degree of confidence. The model is also able to detect new variants of malware that are not present in the training dataset.The model is a significant improvement over traditional malware detection methods, which are often based on signature matching. Signature matching can be effective against known malware, but it is not effective against new malware variants.