Knowledge discovery from big data for intrusion detection using LDA

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper explores a hybrid approach of intrusion detection through knowledge discovery from big data using Latent Dirichlet Allocation (LDA). We identify the 'hidden' patterns of operations conducted by both normal users and malicious users from a large volume of network/systems logs, by mapping this problem to the topic modeling problem and leveraging the well established LDA models and learning algorithms. This new approach potentially completes the strength of signature-based and anomaly-based methods.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014
EditorsPeter Chen, Peter Chen, Hemant Jain
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages760-761
Number of pages2
ISBN (Electronic)9781479950577
DOIs
StatePublished - Sep 22 2014
Event3rd IEEE International Congress on Big Data, BigData Congress 2014 - Anchorage, United States
Duration: Jun 27 2014Jul 2 2014

Publication series

NameProceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014

Other

Other3rd IEEE International Congress on Big Data, BigData Congress 2014
CountryUnited States
CityAnchorage
Period6/27/147/2/14

Keywords

  • LDA
  • big data
  • data mining
  • intrusion detection

ASJC Scopus subject areas

  • Computer Science Applications

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