On Applying Graph Database Time Models for Security Log Analysis

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

For aiding computer security experts in their work, log files are a crucial piece of information. Especially the time domain is of interest, since sometimes, timestamps are the only linking points between associated events caused by attackers, faulty systems or similar. With the idea of storing and analyzing log information in graph databases comes also the question, how to model the time aspect and in particular, how timestamps shall be stored and connected in a proper form. This paper analyzes three different models in which time information extracted from log files can be represented in graph databases and how the data can be retrieved again in a form that is suitable for further analysis. The first model resembles data stored in a relational database, while the second one enhances this approach by applying graph database specific amendments while the last model makes almost full use of a graph database's capabilities. Hereby, the main focus points are laid on the queries for retrieving the data, their complexity, the expressiveness of the underlying data model and the suitability for usage in graph databases.
Original languageEnglish
Title of host publicationFuture Data and Security Engineering
Editors Dang T.K., Küng J., Takizawa M., Chung T.M.
Place of PublicationCham
PublisherSpringer
Pages87-107
Number of pages21
DOIs
Publication statusPublished - 2020

Publication series

NameLecture Notes in Computer Science (LNCS)

Fields of science

  • 202007 Computer integrated manufacturing (CIM)
  • 102001 Artificial intelligence
  • 102006 Computer supported cooperative work (CSCW)
  • 102010 Database systems
  • 102014 Information design
  • 102015 Information systems
  • 102016 IT security
  • 102019 Machine learning
  • 102022 Software development
  • 102025 Distributed systems
  • 102028 Knowledge engineering
  • 102033 Data mining
  • 102035 Data science
  • 502007 E-commerce
  • 505002 Data protection
  • 506002 E-government
  • 509018 Knowledge management

JKU Focus areas

  • Digital Transformation

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