This project is about detecting anomalous behaviour in access log through enhancing the organisation existing methodology. A user or personnel can access different zones in the work place/premises during the course of his/her assigned duties. There are 10s of thousands of personnel, and 100s of zones for access. Each personnel have one of more nature of duty, one or more designations. These requires multi-dimensional analysis. To solve this, we are enhancing the organisation’s existing statistical model/methodology to better capture the underlying patterns. Machine learning and deep learning methodology are currently being explored.
Project Title: Anomaly Detection in Access Control Log of CERTIS Using Machine Learning and AI Techniques
Done By: Certis Technology (Singapore) Pte Ltd, School of Computing, National University of Singapore and Singapore Data Science Consortium (SDSC)
Contact Person: Prof. Tan Kian Lee, Shaw Senior Professor, School of Computing, National University of Singapore & Executive Director, SDSC
This research is supported by the National Research Foundation, Singapore under its Industry Alignment Fund – Pre-positioning (IAF-PP) Funding Initiative.