Presentations Our work in our own words
AI vs AI: Creating Novel Spam and Catching it with Text Generating AI
GPT is a powerful text generation model, but its text generation is unconstrained. This session will discuss how to control GPT to produce sentences which meet specific style or content requirements. The session will review approaches that allow attendees to drive GTP to write SPAM and HAM messages and will demonstrate how to convert GPT into a SPAM filter which detects the generated SPAM.
BSides LV 2021: Bad Neighborhoods – data-driven detection of malicious internet infrastructure
DEF CON 28 AI Village: Detecting hand-crafted social engineering emails with a bleeding-edge neural language model
Learning from Context: A Multi-view Deep Learning Architecture for Malware Detection
Machine learning (ML) used for static portable executable (PE) malware detection typically employs per-file numerical feature vector representations as input […]
Describing Malware via Tagging
Although powerful for conviction of malicious artifacts, machine learning based detection do not generally produce further information about the type […]
Scalable Infrastructure for Malware Labeling and Analysis
One of the best-known secrets of machine learning (ML) is that the most reliable way to get more accurate models […]
Hacking Facial Recognition Systems
Facial recognition is becoming ubiquitous, but how it works is often confused with standard image classification. In this short, high-level […]
Loss is More! Improving Malware Detectors by Learning Additional Tasks
Malware detection is perhaps the most common use case of machine learning for information security (ML-Sec/AI-Sec). ML-Sec malware detectors consist […]
Security data science – Getting the fundamental right
https://www.youtube.com/watch?v=XfT0Ju4vhvI&t=1254 A data science team is now table stakes for most security operations, however data science for security poses unique […]