Real-time Anomaly Detection inHigh-Speed Time-evolving Graphs
Abstract
Real time anomaly detection in graph streams is an important task with high impact applications including cyber security, terrorist detection, fake user detection, etc. In this work, we propose to develop methods to detect anomalies in real time graph streams. For the purpose, we develop local triangle counting algorithms for graph streams, and devise anomaly detection methods based on local triangle counting. Our method has three advantages: 1) it is memory-efficient so that it can run in a system with limited memory, 2) it gives real time response to a query, and 3) it provides high accuracy. The developed method will be a crucial module for real time anomaly detection in high speed time evolving graphs.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 21, 2018
- Source ID
- FA23861614044
Entities
People
- U. Kang
Organizations
- Air Force Office of Scientific Research
- Seoul National University
- United States Air Force