A Multi Agent System for Flow-Based Intrusion Detection Using Reputation and Evolutionary Computation

Abstract

The rising sophistication of cyber threats as well as the improvement of physical computer network properties present increasing challenges to contemporary Intrusion Detection (ID) techniques. To respond to these challenges, a multi agent system (MAS) coupled with flow-based ID techniques may effectively complement traditional ID systems. This paper develops: 1) a scalable software architecture for a new, self-organized, multi agent, flow-based ID system; and 2) a network simulation environment suitable for evaluating implementations of this MAS architecture and for other research purposes. Self-organization is achieved via 1) a "reputation" system that influences agent mobility in the search for effective vantage points in the network; and 2) multi objective evolutionary algorithms that seek effective operational parameter values. This paper illustrates, through quantitative and qualitative evaluation, 1) the conditions for which the reputation system provides a significant benefit; and 2) essential functionality of a complex network simulation environment supporting a broad range of malicious activity scenarios. These results establish an optimistic outlook for further research in flow-based multi agent systems for ID in computer networks.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2011
Accession Number
ADA540167

Entities

People

  • David Hancock

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Cyber
  • Energy and Power Technologies
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computer Languages
  • Computer Network Security
  • Computer Networks
  • Computer Programming
  • Computers
  • Evolutionary Algorithms
  • Genetic Algorithms
  • Information Science
  • Intrusion Detectors
  • Network Protocols
  • Network Science
  • Operating Systems
  • Self Organizing Systems
  • Supervised Machine Learning
  • Transport Protocols

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computer Vision.
  • Theoretical Analysis.

Technology Areas

  • Cyber