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AI for Cybersecurity: Applying Machine Learning to Enhance Malware Analysis 🇬🇧

Time: 11:45 > 12:30
Location: Zone 1 - Gaston Berger conference stage

Malware continues to increase in prevalence and sophistication. VirusTotal reported a daily submission of 2M+ malware samples. Of those 2 million malware daily submissions, over 1 million were unique malware 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. The purpose of this talk is to deliver a new framework that uses Machine Learning models to analyze malware, produce uniform datasets for additional analysis, and classify malicious samples into malware families. Additionally, this research presents a new Ensemble Classification Facility we developed that leverages several Machine Learning models to enhance malware classification. To our knowledge, this is the first research that utilizes Machine Learning to provide enhanced classification of an entire 200+ gigabyte-malware family corpus consisting of 80K+ unique malware samples and 70+ unique malware families. New, labeled datasets are released to aid in future classification of malware. 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.
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This presentation starts with the motivation of why we need Artificial Intelligence to help enhance malware analysis. Then we move into understanding Machine Learning models. From there, we understand how we can write small code stubs to automate malware analysis. We next proceed into live demos that teach how to process the data and standardize the features for our Machine Learning pipeline. We’ll cover model evaluation and then review results of how our Machine Learning models are able to classify malware. We will highlight our results and advancements how malware is classified by our framework.

Solomon SONYA

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