Smart Malware Detection: From Signatures to Artificial Intelligence

Living in the digital era has brought us countless benefits while introducing certain risks. Today, hundreds of thousands of new malware appear every day, thus increasing the risk of data being stolen, corrupted or exploited by malicious entities. While signatures are typically used to detect known malware using anti-virus scanners, this approach is unable to detect new malware (i.e. zero day attacks), encrypted malware, or polymorphic malware able to change its identifiable features or behavior to evade detection. In this work, we propose a smart artificial intelligence based malware detection approach that leverages a combination of machine learning models as well as static and dynamic analysis techniques for the real-time detection of new or polymorphic malware. The system design is elaborated and extensive testing results are presented to showcase the capabilities of our proposed solution. The performance of eight machine learning models are compared to identify the optimal model for static and dynamic malware analysis, thus providing insights on the ability to use machine learning for real-time malware detection.