05-02, 10:00–10:25 (Asia/Jerusalem), PyData Track 1
In the world of malware detection, we need to keep innovating all the time to catch the latest APTs. Let's see how can we do it with recent developments in graph analysis using neural networks
In the last decade, we suffer a new epidemic - Advanced Persistent Threats (APTs).
It seems like every other week a new kind of malware is born and the attack vectors are becoming more and more sophisticated. from pinpoint targeting of specific machines to massive infection of every machine it tackles on its way.
To be able to cope with a large amount of incidents happening every day in the “Everything is connected” age, assigning a human security researcher on every case is expensive and practically impossible.
Although sometimes considered as black magic - In recent years we can see the increasing usage of machine learning for malware detection and classification. The suggested solutions, inspired by various fields as Computer Vision and NLP, are implementing cutting edge solutions into the cybersecurity field.
In this talk, I’ll show how to use graphs to represent malware and how to use graph embeddings and GCN (Graph Convolutional Networks) to tackle such tasks as malware classification and detection, to help security researchers do their job in a faster and more efficient way.
English
Target audience –Security Experts, Data Scientists
I'm interested in Data Science & Machine learning focused on explainability, representation learning, and visualizations.
I hold a BSc in Computer Science from the Technion and currently pursuing his MSc in Information Systems Engineering from Ben-Gurion University.
I'm currently a Data Scientist @ SentinelOne, fighting malwares using Machine Learning for the last 4 years.