Event Details
Log message anomaly detection with fuzzy C-means and MLP
Presenter: Amir Farzad
Supervisor:
Date: Wed, June 9, 2021
Time: 09:30:00 - 00:00:00
Place: ZOOM - Please see below.
ABSTRACT
Zoom meeting link: https://uvic.zoom.us/j/85845396361?pwd=NlNEb0liSGcrVmV5K1FqTUFpVmtVdz09
Meeting ID: 858 4539 6361
Password: 405765
Note: Please log in to Zoom via SSO and your UVic Netlink ID
Abstract:
Log messages are one of the most valuable sources of information in the cloud and other software systems. These logs can be used for audits and ensuring system security. Many millions of log messages are produced each day which makes anomaly detection challenging. Automating the detection of anomalies can save time and money as well as improve detection performance. In this seminar, an anomaly detection method is proposed using radius-based fuzzy C-means with more clusters than the number of data classes and a multilayer perceptron network. The cluster centers and a radius are used to select reliable positive and negative log messages. Moreover, class probabilities are used with an expert to correct the network output for suspect logs. The proposed model is evaluated with three well-known data sets namely, BGL, Openstack and Thunderbird. The results obtained show that this model provides better results than existing methods.