Practical Artificial Intelligence: Machine Learning for Enhanced Malware Detection, Analysis, & Classification
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Presented at SecTor 2024 by
Malware continues to increase in prevalence and sophistication. VirusTotal reported a daily submission of 2M+ potential malware samples. Of those 2 million daily submissions, over 1 million were unique samples. Successfully exploiting networks and systems has become a highly profitable operation for malicious threat actors. Traditional detection mechanisms including antivirus software fail to adequately detect new and varied malware. Artificial Intelligence provides advanced capabilities that can enhance cybersecurity. This talk discusses how to build a new framework that uses Machine Learning models to analyze malware, produce uniform datasets, and classify malicious samples into malware families. Additionally, this talk presents a new ensemble classification model we developed that leverages several Machine Learning models to enhance malware classification. We provide our newly labeled datasets (called MABEL) to be used as a benchmark for Machine Learning modeling. It is time we leverage the capabilities of Artificial Intelligence and Machine Learning to enhance detection and classification of malware. This talk provides a pathway to incorporate Artificial Intelligence into the automated malware analysis domain.