Research
네트워크 매니지먼트를 위한 보안-중심 시스템을 연구합니다. 프라이버시-우선 연합 학습, 검증 가능한 블록체인 무결성, 그래프 기반 위협 분석을 연구하고, 실제 운영 환경에서 동작하는 결과물을 연구합니다.
[모집 분야]
– 석/박사 과정, 학부 연구생, 박사후연구원
– 리서치 엔지니어 (네트워크, 블록체인, 정보보안)
– 방문 연구원 및 산학 협력 파트너
We are driven by values
We design security-first systems for network management—from telemetry and policy automation to resilient, zero-trust operations—and publish reproducible research that ships as operational tools.
Our focus spans privacy-preserving federated learning, verifiable blockchain integrity, and graph-driven analysis of malware and software supply chains across heterogeneous binaries and languages.
We favor explainable methods, rigorous benchmarking, and responsible openness. Ultimately, we turn complex threats into measurable, actionable intelligence that strengthens managed networks in the real world.
Projects
Third-party Library Detection
Data Provenance and Access Control for IoT Devices
Data Integrity and Access Control
Federated Learning for Non-IID Environment
Security by Design
Protection, telemetry, and policy controls built in from first architecture sketch, making systems resilient by default.
Privacy-Fisrt Federated Learning
Training across silos without moving raw data; secure aggregation preserves privacy while delivering robust models.
Verifiable Blockchain Integrity
On-chain data and smart contracts kept tamper-evident and auditable through rigorous verification and continuous monitoring.