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Network Path Estimation in Uncertain Data via Entity Resolution

  • Dean Philp
  • Naomi Chan
  • Wolfgang MayerEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1127)

Abstract

Network Path Estimation is the problem of finding best paths among multiple potential routes between two devices, which is important to cyber situational awareness. In this context, information obtained from multiple sources and at different points in time must be integrated. However, duplicate representations of the same entities in different data sources must be identified and merged to accurately infer and rank network paths. We extend previous work on deterministic rule-based Entity Resolution with similarity flooding principles to obtain a probabilistic entity matching technique. Our approach outperforms the rule-based approach, allows for domain-specific ontologies to be incorporated, and accounts for provenance across data sources. Using the probabilistic resolutions, we rank network paths according to certainty of the resolutions, which improves network path estimation and contributes to cyber situational awareness.

Keywords

Entity Resolution Path Estimation Similarity Flooding Contextualized Data 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Defence Science Technology GroupEdinburghAustralia
  2. 2.University of South AustraliaAdelaideAustralia

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