MINDS: A New Approach to the Information Security Process

Abstract

This paper describes the work the University of Minnesota is doing with the U.S. Army Research Laboratory to advance the state-of-the-art in network intrusion detection. The Minnesota Network Intrusion Detection System (MINDS) is a data mining based system for detecting unusual network behavior, and emerging cyber threats. MINDS is enjoying great operational success in the ARL's Interrogator information assurance architecture and at the University of Minnesota. MINDS routinely detects brand new attacks and other malicious behaviors which could not have been detected by signature based systems. In addition to detecting new attacks MINDS is very effective at discovering rogue communication channels and the exfiltration of data that are very difficult to identify with other tools.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2004
Accession Number
ADA432117

Entities

People

  • E. E. Eilertson
  • K. S. Long
  • L. Ertoz
  • Vipin Kumar

Tags

Communities of Interest

  • C4I
  • Cyber
  • Human Systems

DTIC Thesaurus Topics

  • Anomaly Detection
  • Change Detection
  • Communication Channels
  • Data Mining
  • Detection
  • Detectors
  • False Alarms
  • High Performance Computing
  • Information Security
  • Intrusion
  • Intrusion Detection
  • Intrusion Detection Systems
  • Intrusion Detectors
  • Military Research
  • Reconnaissance
  • Security
  • Warning Systems

Fields of Study

  • Computer science

Readers

  • Cybersecurity.
  • Military History of the United States in the 20th Century.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • Cyber