Prediction of Malware Threats using Machine Learning Techniques

Machine learning has been used for decades to analyze vast datasets, classify and cluster data, and make predictions using algorithms. One of its top use areas is cybersecurity, where it can help detect and prevent destructive threats such as malware. The use of machine learning in cybersecurity has proven to be a powerful tool in detecting and predicting malware attacks. In recent years, the number of Internet users has greatly increased and with it the number of malware attacks. This has made predicting malware a challenge. Consequently, to date, there is still a need to examine the numerous existing MLs’ performance. This study is presented to identify the best classification model for predicting malware using two datasets and 18 different classifiers belonging to six learning strategies. The results showed that the RandomForest classifier had the highest accuracy, precision, recall, F1-measure, and ROC Area metrics, Moreover, Trees and Bayes learning strategies showed the best predictive performance on the two datasets compared with the other five learning strategies.