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

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 fixed/varying 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.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 22, 2018
Accession Number
AD1069876

Entities

People

  • U. Kang

Organizations

  • Seoul National University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Anomaly Detection
  • Change Detection
  • Communication Networks
  • Counting Methods
  • Data Mining
  • Detection
  • Electronic Mail
  • Errors
  • Military Research
  • Networks
  • Teamwork
  • Triangles
  • Universities

Readers

  • Auditory Neuroscience/Auditory Physiology.
  • Computational Modeling and Simulation
  • Research Science/Academic Research