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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Vydáno v:	IEEE Pacific Visualization Symposium s. 191 - 195
Hlavní autoři:	Danneman, Nathan, Gove, Robert
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.04.2022
Témata:	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
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Popis
Shrnutí:	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