Accession Number:

AD1069876

Title:

Real-time Anomaly Detection in High-Speed Time evolving Graphs

Descriptive Note:

Technical Report,23 Sep 2016,22 Sep 2018

Corporate Author:

SEOUL NATIONAL UNIVERSITY SEOUL Korea, South

Personal Author(s):

Report Date:

2018-09-22

Pagination or Media Count:

8.0

Abstract:

The PI was successful in this research grant. The goal of this project was to 1 develop memory-efficient and accurate local triangle counting method in a multigraph stream using fixedvarying sampling rates, and 2 detect anomalies using triangle information. They created and tested two local triangle counting methods MASCOT and FURL. Experimental results demonstrate that FURL provides the best accuracy compared to the state-of-the-art algorithm in a memory-efficient way. The PI has 1 peer reviewed papery published and 1 currently in review as a direct result of this grant award.

Subject Categories:

  • Miscellaneous Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE