Malware detection using opcodes and machine learning

Abstract

Malware detection plays and important role in modern digital systems. Protecting against the fast-paced evolving cyber attacks is critical to safeguard sensitive information and preserve the integrity of digital infrastructure. Traditional signature-based detection methods are not effective when detecting new or altered versions of malware, such as polymorphic or metamorphic malware. Machine learning approaches have been proven to be much more effective at detecting such malware. Runtime behavior can be captured using the most fundamental part of a program, its instructions, also referred as the opcodes. This study presents both static and dynamic analysis using opcodes as the main feature for machine learning models.