Topic Grids for Homogeneous Data Visualization
This addresses anomaly detection and behavior analysis for human experts working with access logs, but appears incremental as it builds on existing topic modeling and visualization techniques.
The paper tackles the problem of detecting anomalies and analyzing behavior from access log content by quantifying content-based behavioral risk in a high-dimensional topic space and projecting topics homogeneously into a human-friendly format.
We propose the topic grids to detect anomaly and analyze the behavior based on the access log content. Content-based behavioral risk is quantified in the high dimensional space where the topics are generated from the log. The topics are being projected homogeneously into a space that is perception- and interaction-friendly to the human experts.