Killed By ML – URL Analysis with LIME
Introduction At Sophos we take a neural network approach to detecting previously unseen malicious and derogatory URLs. We use a […]
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ELI10: Hunting for Malicious URLs with Character-level Embeddings and Convolutions
Introduction On any given day that we are happily browsing the Web, we are stomping around on a minefield. Malware […]
A machine learning approach to inferring the maliciousness of unknown IP addresses, autonomous systems, and ISPs
Introduction The machine learning-based detection technologies we build at Sophos AI rely on many information sources, including binary programs, system […]
Catastrophic Forgetting – Part 2
Introduction In the last blog post (linked here for anyone who missed it), I explained what catastrophic forgetting is and […]
Catastrophic Forgetting – Part 1
Here at the Sophos AI team, our most common goal is to develop deep learning models that inspect a file […]
How SophosAI Stops BEC gift card scams
Gift cards are a favorite way for scammers to squeeze money out of their victims. Unlike wire or bank transfers, […]
Sophos-ReversingLabs (SOREL) 20 Million sample malware dataset
The Sophos AI team is excited to announce the release of SOREL-20M (Sophos-ReversingLabs – 20 million) – a production-scale dataset […]
ML Expectation vs. Reality, Part 2: Doing the Actual Analysis
So, you’ve followed the advice in Part 1 of this series. Now you’ve got a nice big data set and you’re pretty sure that […]
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 […]
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 […]