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Network Path Estimation in Uncertain Data via Entity Resolution

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Data Mining (AusDM 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1127))

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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.

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Notes

  1. 1.

    We computed ranked paths using Neo4j’s All Shortest Paths algorithm [1].

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Correspondence to Wolfgang Mayer .

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Philp, D., Chan, N., Mayer, W. (2019). Network Path Estimation in Uncertain Data via Entity Resolution. In: Le, T., et al. Data Mining. AusDM 2019. Communications in Computer and Information Science, vol 1127. Springer, Singapore. https://doi.org/10.1007/978-981-15-1699-3_16

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  • DOI: https://doi.org/10.1007/978-981-15-1699-3_16

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  • Print ISBN: 978-981-15-1698-6

  • Online ISBN: 978-981-15-1699-3

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