Topological Data Analysis for Enhancing Embedded Analytics for Enterprise Cyber Log Analysis and Forensics Bihl, Trevor Gutierrez, Robert Bauer, Kenneth Boehmke, Brad Saie, Cade 2020-01-04T07:32:51Z 2020-01-04T07:32:51Z 2020-01-07
dc.description.abstract Forensic analysis of logs is one responsibility of an enterprise cyber defense team; inherently, this is a big data task with thousands of events possibly logged in minutes of activity. Logged events range from authorized users typing incorrect passwords to malignant threats. Log analysis is necessary to understand current threats, be proactive against emerging threats, and develop new firewall rules. This paper describes embedded analytics for log analysis, which incorporates five mechanisms: numerical, similarity, graph-based, graphical analysis, and interactive feedback. Topological Data Analysis (TDA) is introduced for log analysis with TDA providing novel graph-based similarity understanding of threats which additionally enables a feedback mechanism to further analyze log files. Using real-world firewall log data from an enterprise-level organization, our end-to-end evaluation shows the effective detection and interpretation of log anomalies via the proposed process, many of which would have otherwise been missed by traditional means.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.238
dc.identifier.isbn 978-0-9981331-3-3
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Cybersecurity and Privacy in Government
dc.subject analytics
dc.subject cyber
dc.subject data mining
dc.subject firewall
dc.subject government
dc.subject topological data analysis
dc.title Topological Data Analysis for Enhancing Embedded Analytics for Enterprise Cyber Log Analysis and Forensics
dc.type Conference Paper
dc.type.dcmi Text
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