AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods.
Appl. Sci., 2023, [DOI]
S. Ndichu, T. Ban, T. Takahashi, and D. Inoue.

Detecting Web-Based Attacks with SHAP and Tree Ensemble Machine Learning Methods.
Appl. Sci., 2022, [DOI]
S. Ndichu, S. Kim, S. Ozawa, T. Ban, T. Takahashi, and D. Inoue.

Deobfuscation, Unpacking, and Decoding of Obfuscated Malicious JavaScript for Machine Learning Models Detection Performance Improvement.
CAAI Transactions on Intelligence Technology, IET Journals, 2020, [DOI]
S. Ndichu, S. Kim, and S. Ozawa.

A Machine Learning Approach to Detection of JavaScript-based Attacks Using AST Features and Paragraph Vectors.
Applied Soft Computing, Elsevier, 2019, [DOI]
S. Ndichu, S. Kim, S. Ozawa, T. Misu, and K. Makishima.

Critical-Threat-Alert Detection using Online Machine Learning.
IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, [DOI]
S. Ndichu, T. Ban, T. Takahashi, and D. Inoue.

Security-Alert Screening with Oversampling Based on Conditional Generative Adversarial Networks.
IEEE Proceedings of the Asia Joint Conference on Information Security (AsiaJCIS), Baoding, China, 2022, [DOI]
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.
IEEE International Conference on Big Data (Big Data), Orlando, Florida, USA, 2021, [DOI]
S. Ndichu, T. Ban, T. Takahashi, and D. Inoue.

Combat Security Alert Fatigue with AI-Assisted Techniques.
In Cyber Security Experimentation and Test Workshop (CSET), ACM, New York, NY, USA, 2021, [DOI]
T. Ban, S. Ndichu, T. Takahashi, and D. Inoue.

A Machine Learning Approach to Malicious JavaScript Detection using Fixed Length Vector Representation.
IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 2018, [DOI].
S. Ndichu, S. Ozawa, T. Misu, and K. Okada.

A Remote Access Security Model based on Vulnerability Management.
International Journal of Information Technology and Computer Science (IJITCS), MECS Press, 2020, [DOI]
S. Ndichu, S. McOyowo, H. Okoyo, and C. Wekesa.

A Domains Approach to Remote Access Logical Vulnerabilities Classification.
International Journal of Computer Network and Information Security (IJCNIS), MECS Press, 2019, [DOI]
S. Ndichu, S. McOyowo, H. Okoyo, and C. Wekesa.

Web-Based Integrated Evaluation Framework for Information Security Preparedness in Law Enforcement Agencies.
International Journal of Computer Science and Information Security (IJCSIS), LJS Publishing, 2016, [URL]
S. Ndichu, and P. Ogao.

A Review of Security Vulnerabilities, Controls, and Models in Networked Environments.
International Journal of Latest Research in Engineering and Technology (IJLRET), 2016, [URL]
S. Ndichu, S. McOyowo, and C. Wekesa.

A Review of Information Security Preparedness Evaluation Mechanisms in Law Enforcement Agencies.
IOSR Journal of Computer Engineering (IOSR-JCE), 2016, [DOI]
S. Ndichu, and P. Ogao.

A Machine Learning Model Performance Improvement Approach to Detection of Obfuscated JavaScript-based Attacks.
Network and Distributed Systems Security Symposium (NDSS), San Diego, CA, USA, 2020.
S. Ndichu, S. Kim, and S. Ozawa.

Malicious JavaScript Detection Using Doc2Vec Feature Learning: Improvement in Detection of Obfuscated Parts through Simplification and Decoding.
International Workshop on Security (IWSEC), Tokyo, Japan, 2019.
S. Ndichu, S. Kim, and S. Ozawa.

Detection of JavaScript-based Attacks Using Doc2vec Feature Learning.
International Workshop on Security (IWSEC), Sendai, Japan, 2018.
S. Ndichu, S. Kim, S. Ozawa, T. Misu, and K. Okada.

Detection of Malicious JavaScript Contents Using Doc2vec Feature Learning.
Symposium on Cryptography and Information Security (SCIS), Niigata, Japan, 2018.
S. Ndichu, S. Ozawa, T. Misu, and K. Okada.