Detect malware using machine learning

I will provide some excel files which have all the features already extracted and the features are in numerical form already. The features have been extracted from malware and non-malware samples (all the files are separate, so malware in one excel file and non-malware in another file), so the idea is to detect and classify the malware and non-malware samples. It is a binary classification problem, and we can label the malware as ‘1’ and the non-malware as ‘0’. The feature selection should be done Manually which will allow us to select the features we want. There are 9 datasets, so we need to train on one of the datasets and create a model. We must then use the model to test the remaining datasets (8 datasets). The ask here is to train on one dataset only and then test on the remaining datasets. Once done, we need to have some way of viewing the test results so we can use confusion matrix and some other visualisation tools to visualize the results. I propose that we use Random Forest, Decision Tree or any other Machine Learning algorithm or even deep learning or a neural network. I want to be able to test at least 3 algorithms.