Younghoo Lee

Senior Data Scientist

Younghoo Lee is a Senior Data Scientist at Sophos. Prior to joining Sophos, he developed malware classification systems at Symantec and mobile software platforms at Samsung Electronics. His research interests include deep learning models for detecting malicious emails and mobile applications and categorizing web content. In his free time, he explores amazing bushwalking tracks in Sydney.

@younghoo_au

Introduction The use of deep neural networks has significantly improved the performance of machine learning in fields such as image […]

Introduction Many organizations are transitioning their security management to the cloud, where it is much easier to collect and access […]

Gift cards are a favorite way for scammers to squeeze money out of their victims. Unlike wire or bank transfers, […]

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.

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.

Younghoo Lee

Blog Posts

GPT-3 and Cybersecurity

A Natural Language Query Interface for XDR/SQL

How SophosAI Stops BEC gift card scams

Presentations

BSides LV 2022: GPT-3 and Me: How Supercomputer-Scale Neural Network Models Apply to Defensive Cybersecurity Problems

AI vs AI: Creating Novel Spam and Catching it with Text Generating AI

DEF CON 28 AI Village: Detecting hand-crafted social engineering emails with a bleeding-edge neural language model

Publications

CatBERT: Context-Aware Tiny BERT for Detecting Targeted Social Engineering Emails

SeqDroid: Obfuscated Android Malware Detection Using Stacked Convolutional and Recurrent Neural Networks