We received a large number of submissions, bear with us
while we’re scooping across the stack!
We’ll update you on your submission status ASAP!
Thank you!
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.
Other Information
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.
https://twitter.com/0xSolomonSonya
https://www.linkedin.com/in/solomon-sonya-a6510224/
We received a large number of submissions, bear with us
while we’re scooping across the stack!
We’ll update you on your submission status ASAP!
Thank you!
Early Birds had been raffled!
STANDARD TICKETS are still available but flying away, quick!
For the 20th year, leHACK will host a prestigious talk lineup.
Wether you are a security researcher, a hacker,a freak, or a unicorn, you can come on stage and share your unique knowledge with your pairs.