Intelligent Network & Security Lab

In a network environment that is evolving into intelligent connectivity by converging rapidly developing AI technology with the Internet of Things (IoT) environment, the center of the 4th industrial era, theoretical and experimental research is conducted on networks built through various technologies and communication protocols. We conduct research on security using ML to analyze performance and solve various existing security issues. We conduct research on AI Convergence Networking, Network Security, IoT, SDN, AI Cryptography, and C-ITS, and based on these technological elements, we provide information on intelligent network configuration and security that promotes AI technology and IT convergence in the future Internet network environment.
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Recent Research Topic

AI Convergence Networking (Network Security)

  • Implement Detecting Network attack through Machine Learning
  • Network Protocol classification through state inference

ML Utilization study

  • N-DQN: Implementation and Research of Hierarchical Parallel Reinforcement Learning Model
  • Music Composition through Reinforcement Learning

AI Cryptography

  • DNN based 3D-Cube Crypto key generation algorithm
  • Deep Vision : Deepfake detection
  • Machine Learning based Image Classification Attack and Protection

Recent Projects

ICT혁신인재4.0

  • 차세대 지능형 모빌리티 융합보안 전문가 교육 사업
  • 과학기술정보통신부, 2020.07.01 ~ 2024.12.31

산업혁신인재성장지원(R&D)

  • 섬유패션산업DX전문인력양성
  • 한국산업기술진흥원, 2024.03.01 ~ 2029.02.28

Recent Publication

  • Li, Jin, et al. "A Time-Sensitive Networking Traffic Scheduling Method Based on Q-Learning Routing Optimization." 2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE, 2024.

  • Kim, Keecheon. "Multi-agent deep q network to enhance the reinforcement learning for delayed reward system." Applied Sciences 12.7 (2022): 3520.

  • Kim, Keecheon. "Enhancing Reinforcement Learning Performance in Delayed Reward System Using DQN and Heuristics." IEEE Access 10 (2022): 50641-50650.

  • Kim, Sang-Won, and Kee-Cheon Kim. "Traffic Type Recognition Method for Unknown Protocol—Applying Fuzzy Inference." Electronics 10.1 (2020): 36.

  • Jung, Tackhyun, Sangwon Kim, and Keecheon Kim. "Deepvision: Deepfakes detection using human eye blinking pattern." IEEE Access 8 (2020): 83144-83154.

Recent Courses

네트워크구조

  • 전반적인 네트워크 구조에 대한 자유로운 토론을 통하여 네트워크 기술 평가 및 개발
  • Graduate

모바일네트워크

  • 모바일네트워크에 대한 전반적인 이해를 바탕으로 기존 기술에 대한 발전 방향 제시
  • Graduate

컴퓨터네트워크

  • 컴퓨터네트워크의 기본적인 지식에 대한 강의
  • Undergraduate, 2 classes