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An automatically tuning intrusion detection system.

Zhenwei Yu1, Jeffrey J P Tsai, Thomas Weigert

  • 1Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA. zyu@cs.uic.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|April 10, 2007
PubMed
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This study introduces an automatically tuning intrusion detection system (ATIDS) that reduces manual effort. The ATIDS enhances security by learning from operator feedback on false predictions, improving performance in dynamic environments.

Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Intrusion detection systems (IDS) are crucial security layers for identifying threats in information systems.
  • Traditional IDS rely heavily on expert knowledge, creating a dependence that hinders scalability.
  • Dynamic environments necessitate continuous model tuning for sustained IDS performance.

Purpose of the Study:

  • To present an automatically tuning intrusion detection system (ATIDS) that reduces manual operator intervention.
  • To enable on-the-fly model tuning based on operator feedback for false predictions.
  • To improve the efficiency and accuracy of intrusion detection in evolving digital landscapes.

Main Methods:

  • Development of an automatically tuning intrusion detection system (ATIDS).

Related Experiment Videos

  • Implementation of an on-the-fly tuning mechanism utilizing operator feedback on misclassifications.
  • Evaluation of the ATIDS using the KDDCup'99 intrusion detection dataset.

    • Main Results:

    • The ATIDS achieved up to a 35% improvement in misclassification cost compared to non-tuning systems.
    • Significant performance gains (around 30%) were observed even with only 10% of false predictions used for tuning.
    • Approximately 20% improvement was realized with minimal tuning data (1.3% of false predictions) when tuning was timely.

    Conclusions:

    • The proposed ATIDS offers a practical solution for enhancing intrusion detection capabilities.
    • Automated tuning significantly improves IDS performance and reduces operator burden.
    • ATIDS allows operators to focus on verifying low-confidence predictions, streamlining the tuning process.