By Topic

Anomaly detection using visualization and machine learning

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
F. Mizoguchi ; Inf. Media Center, Sci. Univ. of Tokyo, Japan

Unauthorized access from inside or outside an organization has become a social problem in the last few years, making a system that can detect such accesses desirable. We therefore monitor normal activities using inductive logic programming (ILP) which is one of machine learning and detect anomalies. To ensure effective monitoring, we think the following two points must be considered. One point is automation of detection by ILP system, which is a rule generation engine, that always induces and updates effective rules. The other point is providing a visualization tool that reflects induced rules to the detection system. This tool enables an administrator to understand detection situations. For automated detection, we provide the ILP system with an automatic parameter adjustment function. For the visualization tool, we apply the visualization technology of a hyperbolic tree

Published in:

Enabling Technologies: Infrastructure for Collaborative Enterprises, 2000. (WET ICE 2000). Proeedings. IEEE 9th International Workshops on

Date of Conference: