Abstract
Recently, malware has become more abundant and complex as the Internet has become more widely used in daily services. Achieving satisfactory accuracy in malware detection is a challenging task since malicious software exhibit non-relevant features when they change the performed behaviors as a result of their awareness of the analysis environments. However, the existing solutions extract features from the entire collected data offered by malware during the run time. Accordingly, the actual malicious behaviors are hidden during the training, leading to a model trained using unrepresentative features. To this end, this study presents a feature extraction scheme based on the proposed dynamic initial evasion behaviors determination (DIEBD) technique to improve the performance of evasive malware detection. To effectively represent evasion behaviors, the collected behaviors are tracked by examining the entropy distributions of APIs-gram features using the box-whisker plot algorithm. A feature set suggested by the DIEBD-based feature extraction scheme is used to train machine learning algorithms to evaluate the proposed scheme. Our experiments' outcomes on a dataset of benign and evasive malware samples show that the proposed scheme achieved an accuracy of 0.967, false positive rate of 0.040, and F1 of 0.975.