IDDM: Intrusion Detection Using Data Mining Techniques

Abstract

The IDDM project aims to determine the feasibility and effectiveness of data mining techniques in real-time intrusion detection and produce solutions for this purpose. Traditionally, data mining is designed to operate on large off-line data sets. Previous attempts to apply the discipline in real-time environments met with varying success. In this paper, we overview earlier attempts to employ data mining principles in intrusion detection and present a possible system architecture for this purpose. As a consequence, we show that by combining data mining algorithms with agent technologies, near real-time operation may be attained.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2001
Accession Number
ADA392237

Entities

People

  • Tamas Abraham

Organizations

  • Defence Science and Technology Group

Tags

Communities of Interest

  • Cyber

DTIC Thesaurus Topics

  • Computer Science
  • Computers
  • Data Mining
  • Data Sets
  • Detection
  • Detectors
  • Electronic Mail
  • False Alarms
  • Information Science
  • Information Systems
  • Intrusion Detection
  • Intrusion Detection Systems
  • Intrusion Detectors
  • Machine Learning
  • Network Protocols
  • Network Science
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Cybersecurity.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML