Designing Size Consistent Statistics for Accurate Anomaly Detection in Dynamic Networks

Fond, Timothy La; Neville, Jennifer; Gallagher, Brian

doi:10.1145/3185059

Title: Designing Size Consistent Statistics for Accurate Anomaly Detection in Dynamic Networks

Journal Article · Mon Apr 16 00:00:00 EDT 2018 · ACM Transactions on Knowledge Discovery from Data

DOI:https://doi.org/10.1145/3185059· OSTI ID:1497323

Fond, Timothy La ^[1]; Neville, Jennifer ^[2]; Gallagher, Brian ^[1]

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Purdue Univ., West Lafayette, IN (United States)

An important task in network analysis is the detection of anomalous events in a network time series. These events could merely be times of interest in the network timeline or they could be examples of malicious activity or network malfunction. Hypothesis testing using network statistics to summarize the behavior of the network provides a robust framework for the anomaly detection decision process. Unfortunately, choosing network statistics that are dependent on confounding factors like the total number of nodes or edges can lead to incorrect conclusions (e.g., false positives and false negatives). In this article, we describe the challenges that face anomaly detection in dynamic network streams regarding confounding factors. We also provide two solutions to avoiding error due to confounding factors: the first is a randomization testing method that controls for confounding factors, and the second is a set of size-consistent network statistics that avoid confounding due to the most common factors, edge count and node count.

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Research Organization:: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1497323

Report Number(s):: LLNL-JRNL-738004; 889202

Journal Information:: ACM Transactions on Knowledge Discovery from Data, Vol. 20, Issue 2; ISSN 1556-4681

Publisher:: Association for Computing MachineryCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 3 works

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