Identifying Useful Features for Malware Detection in the Ember Dataset Many studies have been conducted to detect malware based on machine learning of program features extracted using static analysis. In this study, we consider the task of distinguishing between malware and benign programs by learning their surface features, such as general file information and imported functions. To make such attempts practical, a good balance among accuracy, learning time, and featuredata sizes is required. Although using only a subset of features can reduce the required time and data sizes, it is not trivial to select an appropriate subset of features. In this paper, we present a case study of feature selection in malware detection based on supervised machine learning. We used the Ember dataset as the target data and identified useful combinations of features in terms of accuracy, learning time, and data size.

Ransomware Prediction Using Supervised Learning Algorithms Malware has become most popular attack vector, among which ransomware remained a threat to individuals and organisations. Ransomware main objectives is extortion by imposing some form of denial of service to either the system or system resources such as files until ransom is paid. This make ransomware different from conventional malware that seek to replicate, delete files, exhilarate data or extensively consume system resources. Unfortunately, detection approaches such as sandboxes analysis and pipelines are inadequate due to lack of luxury of being able to isolate a sample to analyse, and when this occurs is already too late for several users.Therefore, machine learning as prove its efficiency and has been used in research for malware detection. In this paper, we explore machine learning algorithms in ransomware detection. Specifically, the data set used contains 30,000 attributes which is to be use as independent variables to predict ransomware.However, since is difficult to incorporate all the attribute in the analysis, we therefore results to use five attribute to serves a proof of concept for feature selection. Then, after feature selection, we apply support vector machine algorithm of which RMSE of 0.179 was obtained and classifying ransomware with 88.2% accuracy. The Support Vector Machine has high performance in detection and classifying ransomware when compare to other machine learning classifier.