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You are here: Home en Syllabuses (2020) Faculty of Engineering Engineering Science Fluid Dynamics1

Fluid Dynamics1

JA | EN

Numbering Code
  • U-ENG25 25142 LJ77
  • U-ENG25 25142 LJ71
Term 2020/Second semester
Number of Credits 2 credits
Course Type Lecture
Target Student Undergraduate
Language Japanese
Day/Period Tue.2
Instructor(s)
  • Graduate School of Engineering, Associate Professor OOWADA TAKU
  • Graduate School of Engineering, Senior Lecturer SUGIMOTO HIROSHI
Schedule and Contents Guidance,2times,Guidance on how this class is operated, and how to use computing facility for this class.\ Basic knowledge on the role of IDS in network security and how machine learning can help the intrusion detection.
Intrusion Detection by Signature-Based IDS,5times,Learn the mechanism of intrusion detection by signature-based IDS by studying open source signature-based IDS and attacks, such as correspondence between alarms issued from IDS and communications, and adding signatures to detect attacks.
Intrusion Detection by Machine Learning,7times,Learn the method of classifying normal and malicious traffic by machine learning algorithms and public dataset for benchmarking intrusion detection performance.
Presentation,1time,Based on the exercise, students presents their methods of intrusion detection using machine learning, and discuss it with other students and instructors.
Prerequisites None