Bio-Inspired Distributed Decision Algorithms for Anomaly Detection

Abstract

This research effort brought together computer scientists and biologists to investigate the potential for self-organizing anomaly detection protocols inspired by those observed naturally in colonies of social insects to provide appropriate, dynamic, detection thresholds for anomalous event patterns on computer system networks to improve early detection and rejection methods to counter malicious threats.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2017
Accession Number
AD1030768

Entities

People

  • Nina H. Fefferman

Organizations

  • Rutgers University

Tags

Communities of Interest

  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Anomaly Detection
  • Change Detection
  • Computer Communications
  • Computer Networks
  • Computers
  • Detection
  • Detectors
  • Information Exchange
  • Network Protocols
  • Network Topology
  • Operations Research
  • Port Scanners
  • Star Networks

Fields of Study

  • Computer science

Readers

  • Aquatic Ecology
  • Computer Networking
  • Sensor Fusion and Tracking Systems.