ML Expectation vs. Reality, Part 1: Don’t build a house on sand!
Machine Learning has seen a huge boom in the past decade, with many industries now investing heavily in Machine Learning […]
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How much malware is out there, anyway?
Introduction When we move machine learning models from the lab to the real world, tracking and evaluating model performance becomes […]
Debugging Deep Learning Models
Attention conservation notice: This is a slightly expanded version of a Twitter thread I posted back in January 2020. If […]
Lessons learned from building a 4,000+ member cybersecurity volunteer organization in four months
When I posted this tweet in March of this year, kicking off a process which would give birth to the […]
Detecting Malicious URLs and Stopping the Attack Early
Any good attack-chain usually involves tricking users at some point, whether it’s getting them to run a malicious file because […]
The File path Model: Using Context To Help Convict Malware
In the wild, we often see that malware in user systems persists well hidden in obfuscated or randomized file locations. […]
Are you on target to achieve your strategic initiatives? Methods from our practice at Sophos AI
How we leverage tools and techniques to get real-time forecasts for Data Science initiatives
CATBERT — Detecting malicious emails with a bleeding-edge neural language model
Introduction Malicious emails have been widely used by cybercriminals to attack numerous victims using techniques such as fraud, phishing and […]
Leaner, meaner, better models with model compression
Deep learning models can produce state of the art performance for malware detection, but that performance comes at a price: […]