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Intrusion detection using rough set classification.

Lian-hua Zhang1, Guan-hua Zhang, Jie Zhang

  • 1Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200030, China. a000309035@21cn.com

Journal of Zhejiang University. Science
|August 24, 2004
PubMed
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Rough Set Classification (RSC) effectively ranks features for intrusion detection models. This machine learning approach, enhanced by a hybrid genetic algorithm, achieves over 99% accuracy in detecting network attacks.

Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Machine learning is crucial for detecting network intrusions, encompassing both misuse and anomaly detection.
  • Effective feature selection is vital for building accurate intrusion detection models.

Purpose of the Study:

  • To introduce Rough Set Classification (RSC) for feature ranking and intrusion detection model generation.
  • To improve the efficiency and speed of RSC using a hybrid genetic algorithm.

Main Methods:

  • RSC was employed for feature ranking, converting the process into a minimal hitting set problem solved by a genetic algorithm (GA).
  • A hybrid GA was developed to accelerate RSC's convergence and reduce training time.
  • Intrusion detection models were generated in an interpretable "IF-THEN" rule format.

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Main Results:

  • RSC demonstrated efficient feature ranking, avoiding iterative feature removal common in methods like Support Vector Machine (SVM).
  • The hybrid GA enhanced RSC's convergence speed and decreased training duration.
  • Comparative tests on DARPA benchmark data showed RSC achieved high accuracy (over 99%) for Probe and DoS attacks, comparable to SVM.

Conclusions:

  • RSC offers an efficient and interpretable method for intrusion detection model development.
  • The hybrid GA significantly optimizes RSC's performance.
  • RSC is a viable and accurate alternative to traditional methods like SVM for network intrusion detection.