Blockchain technology has evolved from a nascent concept aimed at securing decentralized digital assets to a powerful framework for enhancing security in diverse domains.
This talk explores the dual trajectory of blockchain’s role in security: from addressing inherent vulnerabilities within blockchain protocols to using blockchain as a security-enabling infrastructure.
First, we examine key advancements in securing blockchain systems, including cryptographic safeguards, and how AI can contribute to detecting threats and enhancing overall security.
Then, we shift focus to emerging applications where blockchain serves as a foundational layer for implementing secure data sharing and privacy-preserving protocols in AI frameworks such as federated learning.
By highlighting both aspects, this talk underscores the transformative potential of blockchain as both a subject and a tool for advancing security in distributed systems.

As attackers change their strategies over time, the threats in cybersecurity continually evolve. Artificial Intelligence (AI) enhances cybersecurity by autonomously detecting and responding to threats, hence augmenting the capacity to defend against various cyberattacks in a dynamic manner. This talk emphasizes the benefits of AI-assisted systems that effectively manage complex cyberattacks while minimizing human errors. I also address the risks associated with adversarial AI and the ethical implications for data privacy. The talk finishes by forecasting the necessity of integrating AI into cybersecurity systems as a pivotal element for ensuring a secure future Internet.

With the expanding adoption of large language models (LLMs) in the public sector, ensuring security and data sovereignty requires a domestically controlled, network-separated closed infrastructure. Rather than one-off development, a sustainable operational ecosystem must be established.

[Directions for Building a Sustainable Foundation Model]
Public-private collaboration is essential for developing domain-specific LLMs tailored to high-risk and low-profit areas (e.g., healthcare, legal sectors), based on open-source LLMs such as LLaMA, DeepSeek, LG EXAONE, and Naver HyperCLOVA X.

[Five Strategic Frameworks for Public LLM Development]
1. **Model/Architecture**: Build internal capabilities and flexible architecture to incorporate cutting-edge technologies.
2. **Data**: Establish a high-quality public data lake through refined integration.
3. **Infrastructure**: Expand secure infrastructures based on GPU, on-premise, and network separation.
4. **Talent & Organization**: Train in-house experts and build a collaborative public-private system.
5. **Policy & Regulation**: Institutionalize legal frameworks and operational standards for research data.

The research focuses on strategies for securing autonomy, sustainability, and security in public-sector AI — paving the way toward national AI sovereignty and enhanced global competitiveness.

Many students today begin their journey into hacking through CTF (Capture The Flag) competitions, sharpening their skills by solving complex and challenging problems. However, a common question arises: ""How does this connect to my future career?"" ""What more should I do to grow as a professional hacker?"" In an era where technology evolves rapidly and AI is transforming the landscape—including cybersecurity—these concerns are more relevant than ever: ""How can I survive and thrive in the midst of such change?"" In this talk, the speaker shares personal experiences—first as a student who grew through CTFs, and now as a professor mentoring future hackers. The session aims to provide insights and practical advice on building a meaningful career in cybersecurity, navigating industry shifts, and finding your own path as a hacker in the age of AI.

Generative AI is driving innovation across various industries, including the field of cybersecurity. While it offers powerful tools for both offense and defense, it also introduces new and significant security challenges.

This talk explores the dual dimensions of security in the era of Generative AI:
1. **AI for Security** – the use of AI technologies to enhance cybersecurity capabilities, such as phishing detection, malware generation, code auditing, and log analysis.
2. **Security for AI** – addressing vulnerabilities in AI systems themselves, including prompt injection, data poisoning, and corresponding defense mechanisms like guardrails and robust alignment strategies.

By examining both how AI can be used to secure systems and how AI systems must be secured, this presentation offers a comprehensive overview of cybersecurity in the age of Generative AI.

With the rapid advancement of quantum computing, concerns are growing over the vulnerability of existing public-key cryptographic systems. To prepare for potential threats in the quantum era, researchers and institutions are actively exploring various countermeasures.

This talk introduces the fundamentals of quantum computing and recent developments in quantum software. It then examines the current state of post-quantum cryptography (PQC) standardization efforts aimed at securing systems against quantum-enabled attacks. Additionally, the session will explore emerging security technologies that leverage the unique characteristics of quantum mechanics.

Currently, more than 7,000 low Earth orbit (LEO) satellites are in operation, and the number is rapidly increasing each year with the expansion of networks like Starlink. Despite this growth, there is limited public knowledge about the nature of the data transmitted and received through these satellites.

This presentation briefly shares preliminary findings from our research team’s recent project on collecting and analyzing LEO satellite signals. While still in its early stages, we believe that large-scale satellite signal analysis will play a crucial role in shaping the future of space cybersecurity.