Event Details
Unsupervised log message anomaly detection with Isolation Forest and Autoencoders
Presenter: Amir Farzad
Supervisor:
Date: Tue, May 25, 2021
Time: 16:00:00 - 00:00:00
Place: ZOOM - Please see below.
ABSTRACT
Zoom meeting link: https://uvic.zoom.us/j/81817561590?pwd=OUFOalcxRTNCVDYzODQ0WVlka29iQT09
Meeting ID: 818 1756 1590
Password: 163236
Note: Please log in to Zoom via SSO and your UVic Netlink ID
Abstract:
Log messages are now broadly used in cloud and software systems. They are important for classification and anomaly detection as millions of logs are generated each day. In this seminar, an unsupervised model for log message anomaly detection is proposed which employs Isolation Forest and two deep Autoencoder networks. The Autoencoder networks are used for training and feature extraction, and then for anomaly detection, while Isolation Forest is used for positive sample prediction. The proposed model is evaluated using the BGL, Openstack and Thunderbird log message data sets. The results obtained show that the number of negative samples predicted to be positive is low, especially with Isolation Forest and one Autoencoder. Further, the results are better than with other well-known models.