Comparative analysis of various machine learning algorithms for ransomware detection

Abstract

Recently, the ransomware attack posed a serious threat that targets a wide range of organizations and individuals for financial gain. So, there is a real need to initiate more innovative methods that are capable of proactively detect and prevent this type of attack. Multiple approaches were innovated to detect attacks using different techniques. One of these techniques is machine learning techniques which provide reasonable results, in most attack detection systems. In the current article, different machine learning techniques are tested to analyze its ability in a detection ransomware attack. The top 1000 features extracted from raw byte with the use of gain ratio as a feature selection method. Three different classifiers (decision tree (J48), random forest, radial basis function (RBF) network) available in Waikato Environment for Knowledge Analysis (WEKA) based machine learning tool are evaluated to achieve significant detection accuracy of ransomware. The result shows that random forest gave the best detection accuracy almost around 98%.