Cyber Information Security Laboratory
Research Field
Van-Linh Nguyen (S'16-M'19 -SM'24) is an Assistant Professor at the Department of Computer Science and Information Engineering, National Chung Cheng University (CCU), Taiwan, and the lead of the Cyber Information Security Laboratory (CIS Lab). Prior to joining CCU as a faculty, he was a lecturer at the Department of Information Technology, Thai Nguyen University of Information and Communication Technology (TNU-ICTU), Vietnam, from 2012 to 2022. He was also a postdoctoral fellow with CCU from 2020 to 2022. He received his Ph.D. in computer science and information engineering from National Chung Cheng University, Taiwan, in 2019. He has actively served as a reviewer for flagship TVT, COMMAG, COMST, COMML, and participated as a Technical Program Committee Member for a variety of international conferences, such as CISC 2023, ICTA 2023, and CITA 2024. He is a guest editor of The Internet of Military Defense Things special issue in IEEE Internet Of Things Magazine. His broad research interests include physical layer security, intrusion detection systems in IoT, intelligent transportation systems and vehicular security, security in aerial-assisted B5G/6G networks, and military communications.
CIS Lab is a research-oriented lab at National Chung Cheng University established by Prof. Van-Linh Nguyen. Students are diverse and come from many countries, e.g., Taiwan, Vietnam, Thailand, Indonesia, Malaysia, Ethiopia, Iran, and India. Interns also include students from Indonesia, Czech, Germany, and Japan. In 2024, there is a total of 24 students from 8 countries to do internship at CISLab.
Lab WEBSITE and photos: https://ccucyberseclab.github.io
Research topics
Topic 1: Trustable Artificial Intelligence for Critical Applications and Misbehavior Detection the Quantum Era
Artificial Intelligence (AI) technologies (Vision Transformer, ChatGPT, LLM), 6G networking, and quantum computing are the leading forces in bringing the world to the era of better intelligence and full automation. However, the rapid development of such technologies raises concerns that they could be used to damage human life, destroy critical infrastructure, and further violate user privacy. For example, AI power can be exploited to scan the vulnerabilities of critical control systems (SCADA, ITS) or track a target user in a restricted access building, even without physical intrusion. Similarly, the attackers can launch adversarial attacks against AI-based Advanced Driver-Assistance Systems (ADAS) and force connected vehicles to act as unexpected weapons to hit civilians. Early detection of security attacks and secure AI models are the top targets of many current research efforts. In short, this project encourages the talents who are interested in the following topics:
(1) AI for Cybersecurity: Misbehavior detection in autonomous vehicles, Deep Reinforcement Learning for aerial-assisted networks (UAV-satellite-space) or Intelligent Transportation Systems, Self-supervised Learning, autoDL /ML for Intrusion Detection Systems.
(2) Cybersecurity for AI: Trustable AI for automated vehicles and AI-based control systems from adversarial attacks.
(3) 6G security: Signal sensing, physical layer authentication, high-accuracy localization and sensing.
(4) Space and Quantum security: Blockchain for vehicular/aerial networks; Quantum compatible IDS platforms.
(5) Trustable AI for critical applications: Vision Transformer, ChatGPT, LLM for smart grid, smart health, intelligent transportation, smart manufacturing.
Topic 2: Computer vision and generative AI for smart manufacturing and autonomous driving
Artificial Intelligence is now reaching many applications in our society's life. Many AI-based applications, such as ChatGPT, can provide great answers to many difficult questions beyond average human capability. However, AI has not yet performed well in what humans can do easily, e.g., help robots move smoothly, and drive the car in complex environments. Further, AI requires very large training and extensive computing resources, which not every lab can do. This project aims to propose usable AI and tiny AI to solve our common problems that make AI more reachable and affordable. This topic can cover the following issues:
(1) Usable AI: Skeleton authentication, facial authentication, object segmentation for automated vehicles, multimodal fusion AI for autonomous driving, Vision transformers for defect detections.
(2) Tiny AI: Lightweight AI for IoT devices and microcontrollers in smart manufacturing.
(3) Quantum AI: Quantum machine learning, Quantum vision transformer, quantum adversarial attacks.
- IEEE Senior Member (Institute of Electrical and Electronics Engineers
Ph.D. in Computer Science and Information Engineering
2 Vacancies
Job Description
You are encouraged to do research and implement a prototype on the following specific issues:
(1) Optimization algorithms for quantum machine learning and quantum adversarial attacks, quantum hybrid models for IDS
(2) Product defect/ransomware detection with LLMs and transformer-based models.
(3) User tracking and signal sensing in USRP-based B5G/6G testbeds
(4) Misbehavior detection for O-RAN AI-based applications
Preferred Intern Education Level
- Final-year undergraduate students (who want to pursue MSc in CCU)
- Graduate candidates (who want to pursue PhD in CCU)
- Ph.D. students/postdocs (with publications)
Skill sets or Qualities
1. Strong interest in computer science, computer networks, AI, and cybersecurity.
2. Background knowledge in networking/security, mathematics, optimization, quantum, and computer vision.
3. Publications in my research field
4. International English proficiency certificate (TOEIC >= 750, IELTS >=6.0, TOEFL iBT >= 80 )