SAMUEL NDICHUディーチェサミュエル (ディーチェサミュエル)

サイバーセキュリティ研究所
サイバーセキュリティ研究室　研究員

Samuel Ndichu received the Ph.D. degree from the Graduate School of Engineering, Kobe University in 2021. His doctoral advisor was Professor Seiichi Ozawa. He is currently a researcher at the Cybersecurity Research Institute. He has published more than ten journal and conference papers. His research interests include machine learning, imbalanced learning, and cybersecurity. He is a member of ACM, ISACA, and EC-Council.

研究分野: Machine Learning, Cyber Security, Imbalanced Learning, Alert Screening
キーワード: Machine Learning, Cyber Security, Imbalanced Learning, Alert Screening

selected papers主要論文

[Under revision] S. Ndichu, T. Ban, T. Takahashi and D. Inoue, “Critical threat alerts detection with imbalanced learning,” International Journal of Information Security, 2022.
S. Ndichu, T. Ban, T. Takahashi and D. Inoue, “Security-Alert Screening with Oversampling Based on Conditional Generative Adversarial Networks,” The 17th Asia Joint Conference on Information Security (AsiaJCIS 2022), Baoding, China, August 15-16, 2022.
Ndichu, S.; Kim, S.; Ozawa, S.; Ban, T.; Takahashi, T.; Inoue, D. Detecting Web-Based Attacks with SHAP and Tree Ensemble Machine Learning Methods. Appl. Sci. 2022, 12, 60.
S. Ndichu, T. Ban, T. Takahashi and D. Inoue, “A Machine Learning Approach to Detection of Critical Alerts from Imbalanced Multi-Appliance Threat Alert Logs,” 2021 IEEE International Conference on Big Data (Big Data), 2021, Orlando, FL, USA, pp. 2119-2127.
Tao Ban, Ndichu Samuel, Takeshi Takahashi, and Daisuke Inoue, “Combat Security Alert Fatigue with AI-Assisted Techniques,” In Cyber Security Experimentation and Test Workshop (CSET 2021), Association for Computing Machinery, New York, NY, USA, 9–16.

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