Author Joshua Saxe
BSides LV 2022: Security AI in the Real World: Lessons Learned from Building Practical Machine Learning Systems Deployed to Hundreds of Thousands of Networks
CatBERT: Context-Aware Tiny BERT for Detecting Targeted Social Engineering Emails
Targeted phishing emails are a major cyber threat on the Internet today and are insufficiently addressed by current defenses. In this paper, we leverage industrial-scale datasets from Sophos cloud email security service, which defends tens of millions of customer mailboxes, to propose a novel Transformer-based architecture for detecting targeted phishing emails. Our model leverages both natural language and email header inputs, is more computationally efficient than competing transformer approaches, and we show that it is less prone to adversarial attacks which deliberately replace keywords with typos or synonyms.
Test your speed and cybersecurity savvy against our machine learning model
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 […]
DEF CON 28 AI Village: Detecting hand-crafted social engineering emails with a bleeding-edge neural language model
Garbage in, garbage out: how purportedly great ML models can be screwed up by bad data
SeqDroid: Obfuscated Android Malware Detection Using Stacked Convolutional and Recurrent Neural Networks
A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content
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 […]