Anomaly Behavior Discovery

Advanced Machine Learning and Intelligent Decision Support Systems for User Anomaly Behavior Discovery in Large Transaction System

Our projects aim at assisting system administrators to enhance the security policies of their systems. In such environments, the authenticated and authorized users (trusted users) in one system can access to the resources of other systems and cause serious damage. The security administrators cannot effectively monitor and control such situations due to huge volumes of dynamically changing user behaviors and intentions.

Project Areas

We focus on various project types:

These projects utilize artificial intelligence techniques (data mining and machine learning) through a high-performance computing planform including Apache Spark for high-speed in-memory computing and Nvidia DGX machine learning and GPU technologies.​

Publications

UDATE PROJECT TITLE

B. Eapen, N. Archer, K. Sartipi | February 2021 | Journal of JMIR Dermatol 2020

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B. Eapen, K. Sartipi, N. Archer | May 2020 | ArXiv.org

Research Team

Eduardo Lopez

PHD CANDIDATE

Information systems
DEGROOTE SCHOOL OF BUSINESS
McMaster University

Dr. Kamran Sartipi

Adjunct associate professor

Information systems
DEGROOTE SCHOOL OF BUSINESS
McMaster University

Dr. Norm Archer

PROFESSOR EMERITUS

Information systems
DEGROOTE SCHOOL OF BUSINESS
MCMASTER UNIVERSITY

Other projects

A multi-disciplinary research initiative on Information and System Intelligence research.