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Understanding Cybersecurity Threat Trends Through Dynamic Topic Modeling.

Jennifer Sleeman¹, Tim Finin¹, Milton Halem¹

¹Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, United States.

Frontiers in Big Data

|July 16, 2021

Summary

Tracking cybersecurity threats requires understanding evolving vulnerabilities. Dynamic topic modeling reveals how exploit reports and research papers change over time, aiding threat detection and knowledge discovery.

Keywords:

cybersecurity cyberthreat information dynamic topic modeling knowledge graph topic modeling

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Area of Science:

Computer Science
Information Security

Background:

Cybersecurity threats are escalating, impacting all facets of modern life.
Understanding the evolution of vulnerabilities and exploits is crucial for developing effective countermeasures.
Existing methods for analyzing cybersecurity trends often lack temporal and cross-corpus integration.

Purpose of the Study:

To apply dynamic topic modeling to analyze evolving cybersecurity concepts across different document types.
To integrate temporal topic modeling insights into a semantic knowledge graph for enhanced discovery.
To demonstrate how domain concept seeding improves topic model quality and knowledge graph utility.

Main Methods:

Utilized dynamic topic modeling on two distinct time-stamped cybersecurity corpora: exploit reports and research papers.
Constructed a semantic knowledge graph to represent documents, concepts, and topic modeling outputs.
Employed Wikipedia concepts for domain-specific phrase extraction to seed the knowledge graph and guide topic modeling.
Correlated temporal trends between the two corpora using the generated knowledge graph.

Main Results:

Identified evolving patterns and changing significance of cybersecurity concepts over time.
Demonstrated improved topic model quality and relevance through concept phrase extraction.
Successfully integrated disparate cybersecurity information into a unified knowledge graph.
Showcased the ability to discover cross-corpus relationships and temporal trends.

Conclusions:

Dynamic topic modeling, when integrated into a knowledge graph, provides valuable insights into the evolution of cybersecurity threats.
Seeding knowledge graphs with domain concepts enhances the accuracy and interpretability of topic models.
This approach offers a novel method for relating documents across diverse corpora and uncovering temporal trends in cybersecurity research and incidents.