Security × Machine Learning

Chaeyoung Lee
Security & ML researcher

M.S. student in the Division of Artificial Intelligence Engineering at Sookmyung Women's University and a graduate researcher at SNSec Lab, after graduating early with a B.S. from the same division in Aug 2026 (minoring in Big Data). I work on data-driven security with machine learning — building robust, explainable systems that detect and explain intrusions, from connected vehicles to evolving network threats.

Key Research Areas

Core Tech

Deep Learning, NLP

Security

Data-driven Security, Intrusion Detection

Safety & Trust

Adversarial Machine Learning, Automotive Security

Emerging Interests

Data Poisoning, Machine Unlearning

News

see all →

Jul 15, 2026 Our paper, “Cleansing Lable Contamination in Popularity-based Ranking Lists for Robust DGA Detection” (Chaeri Jung*, Chaeyoung Lee*, Seonghoon Jeong), has been accepted at the IEEE International Conference on Future Machine Learning and Data Science (FMLDS) 2026. *Equal contribution
Jul 8, 2026 Our paper, “DRIFT: Drift-Resilient Invariant-Feature Transformer for DGA Detection” (Chaeyoung Lee*, Chaeri Jung*, Seonghoon Jeong), is now published on IEEE Xplore. *Equal contribution
Jul 8, 2026 Attended a special session on reverse engineering for 5 days, covering how computers process binaries — including x32dbg, PE file structure, control flow graphs (CFGs), BinDiff.
Jun 3, 2026 Awarded an IEEE/IFIP DSN 2026 Student Travel Grant — supporting travel to present “DRIFT: Drift-Resilient Invariant-Feature Transformer for DGA Detection” at the conference in Charlotte, North Carolina. Chaeri Jung and I were each selected by the DSN’26 Student Travel Grant Committee, co-chaired by Meera Sridhar and Domenico Cotroneo.
Jun 3, 2026 Launched this personal website at chaeyoung.net (built June 1–3).

Selected publications