Tuning Automatic Summarization for Incident Report Visualization

We present a machine learning approach to improve the accuracy of summarized incident report visualizations for cyber security. We extend a recent incident report summarization method by training a Bayesian hierarchical model to optimize the summarization algorithm's weights. We also train a fl...

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Bibliographic Details
Published in:IEEE Pacific Visualization Symposium pp. 191 - 195
Main Authors: Danneman, Nathan, Gove, Robert
Format: Conference Proceeding
Language:English
Published: IEEE 01.04.2022
Subjects:
Human computer interaction
Human-centered computing-Visualization-Visualization application domains-Information visualization
Machine learning
Machine learning algorithms
Malware
Neural networks
Security and privacy-Intrusion/anomaly detection and malware mitigation
Security and privacy-Usability in security and privacy
Training
Visualization
ISSN:2165-8773
Online Access:Get full text
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Summary:We present a machine learning approach to improve the accuracy of summarized incident report visualizations for cyber security. We extend a recent incident report summarization method by training a Bayesian hierarchical model to optimize the summarization algorithm's weights. We also train a flat model and a neural network as alternative models to compare against our hierarchical model. Summaries generated by our hierarchical model achieve higher accuracy than the other methods, with an AUC 0.2 higher than the unweighted method while achieving comparable summarization size. We further demonstrate that visualizations of the hierarchical model's summaries are at least as useful the unweighted method's summaries, and possibly more useful.
ISSN:2165-8773
DOI:10.1109/PacificVis53943.2022.00031