Empirical Analysis for Investigating the Effect of Machine Learning Techniques on Malware Prediction

: Malware is used to attack computer systems and network infrastructure. Therefore, classifying malware is essential for stopping hostile attacks. From money transactions to personal information, everything is shared and stored in cyberspace. This has led to increased and more innovative malware attacks. Advanced packing and obfuscation methods are being used by malware variants to get access to private information for profit. There is an urgent need for better software security. In this paper, we identify the best ML techniques that can be used in combination with various ML and ensemble classifiers for malware classification. The goal of this work is to identify the ideal ML pipeline for detecting the family of malware. The best tools for describing malware activity are application programming interfaces (APIs). However, creating API call attributes for classification algorithms to achieve high accuracy is challenging. The experimental results demonstrate that the proposed ML pipeline may effectively and accurately categorize malware, producing state-of-the-art results.