people

Seonghoon Jeong

seonghoon [a.t.] snsec.net

From September 2024, I work at Division of Artificial Intelligence Engineering, Sookmyung Women’s University (SMWU), Seoul, Republic of Korea, as an assistant professor.

Research goal

I investigate and address cybersecurity challenges in Internet service applications through a data-driven approach that utilizes machine learning and deep learning methodologies. My research involves analyzing massive live data streams to secure computer systems and networks from evolving threats, leveraging experience with diverse datasets including massive online games, root DNS servers, mobile payment transactions, and car hacking activities. I have specialized in identifying and explaining intrusions in connected vehicles, notably employing anomaly detection techniques trained solely on benign data. (ORCID: 0000-0001-5638-2851)

My current research interests center on Trustworthy Network Intrusion Detection using Foundation Models. I aim to move beyond simple classification accuracy to build systems that are robust, adaptive, and explainable. Key areas include:

• Multi-modal Traffic Representation & Pre-training: Integrating payload bytes, packet sequences, and protocol metadata to learn comprehensive traffic representations without information loss.
• Drift Resilience & Efficient Adaptation: Developing unsupervised metrics to detect concept drift in real-time and utilizing parameter-efficient fine-tuning (PEFT) to adapt models to new environments with minimal cost.
• Generative & Causal Reasoning: Applying generative AI to not only detect threats but also explain their causes (causal reasoning) and suggest response scenarios.

I am currently working on an Explainable Unsupervised IDS for Automotive Ethernet, which applies these foundation model principles to secure in-vehicle networks against complex intrusions. I am also developing DRIFT (Drift-Resilient Invariant-Feature Transformer), an advanced DGA detector. DRIFT employs a hybrid tokenization strategy (character + subword) and multi-task self-supervised pre-training to maintain robust detection performance against temporally evolving domain generation algorithms.

Education

• Ph.D., Information Security, Korea University, 2023 (Advisor: Prof. Huy Kang Kim)
• M.S., Information Security, Korea University, 2017 (Advisor: Prof. Huy Kang Kim)
• B.S., Information and Communication Engineering, Chungbuk National University, 2015 (Advisor: Prof. Min Choi)

Chaeri Jung

chaerry502 [a.t.] snsec.net

0009-0006-0247-5167

Personal website

Google Scholar

Interests

• Core Tech: Deep Learning, Self-Supervised Learning
• Security Focus: Anomaly Detection, Intrusion Detection
• Future Interests: Adversarial Machine Learning, Concept Drift Adaptation

Education

• Undergraduate Student, Division of Artificial Intelligence Engineering, Sookmyung Women’s University, 2023–Current
• Double Major in Big Data

GitHub Activity

Chaeyoung Lee

amy8985 [a.t.] snsec.net

0009-0006-8907-6504

Google Scholar

Interests

• Core Tech: Deep Learning, NLP
• Security: Data-driven Security, Intrusion Detection
• Safety & Trust: Adversarial Machine Learning, Automotive Security
• Emerging Interests: Data Poisoning, Machine Unlearning

Education

• Undergraduate Student, Division of Artificial Intelligence Engineering (minoring in Big Data), Sookmyung Women’s University, 2023–Current

Previous Affiliation & Activities

• DevOps Engineer, (a Sookmyung Women’s University Community Platform)
  • Maintained scalable AWS infrastructure and CI/CD pipelines using GitHub Actions.
  • Built real-time monitoring systems (Prometheus, Grafana, Loki)
  • Cost measurement and optimization via automated Discord bots.
  • Review on DevOps activities

GitHub Activity

Jisoo Kim

sallysooo [a.t.] snsec.net

Personal website

Interests

• Core Research: Anomaly Detection, Data-driven Security
• Security Applications: Bot Detection, Intrusion Detection, Network Security
• Emerging Interests: LLM Security

Education

• Undergraduate Student, Department of Data Science, Sookmyung Women’s University, 2023–Current

Activities

• Web Hacking Academic Director & Member of (Sookmyung Information Security Study)
• Featured in Sookmyung Women’s University Official Brand Video (2026)

GitHub Activity

Semin Sohn

minse [a.t.] snsec.net

Interests

• Core Interests: Deep Learning Architecture (Autoencoders), Database Systems
• Security Exploration: Software Reverse Engineering, Data-driven Security

Education

• Undergraduate Student, Division of Artificial Intelligence Engineering, Sookmyung Women’s University, 2024–Current
• Double Major in Mathematics

GitHub Activity