Topic Grids for Homogeneous Data Visualization

TL;DR

This paper introduces topic grids, a visualization method for detecting anomalies and analyzing user behavior from access logs by projecting high-dimensional topic data into an intuitive, human-friendly space.

Contribution

It presents a novel approach to visualize high-dimensional log data using topic grids, enhancing anomaly detection and behavioral analysis.

Findings

Effective detection of anomalies in access logs

Improved interpretability of behavioral patterns

High-dimensional topic projection into human-friendly space

Abstract

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.