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Machine-Learning Approach to Optimize SMOTE Ratio in Class Imbalance Dataset for Intrusion Detection.

Jae-Hyun Seo1, Yong-Hyuk Kim2

  • 1Department of Computer Science and Engineering, Wonkwang University, 460 Iksandae-ro, Iksan-si, Jeonbuk 54649, Republic of Korea.

Computational Intelligence and Neuroscience
|December 6, 2018
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Summary
This summary is machine-generated.

This study optimizes the Synthetic Minority Oversampling Technique (SMOTE) ratios for rare classes in the KDD CUP 1999 intrusion detection dataset. The novel approach significantly improves machine learning model performance for network security.

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Area of Science:

  • Computer Science
  • Data Mining
  • Network Security

Background:

  • The KDD CUP 1999 dataset is a benchmark for intrusion detection systems.
  • This dataset suffers from significant class imbalance, with rare classes like User to Root (U2R), Remote to Local (R2L), and Probe attacks comprising less than 1% of instances.
  • Class imbalance hinders the performance of machine learning models in detecting these rare but critical threats.

Purpose of the Study:

  • To address the class imbalance problem in the KDD CUP 1999 intrusion detection dataset.
  • To optimize the Synthetic Minority Oversampling Technique (SMOTE) ratios specifically for rare attack classes (U2R, R2L, Probe).
  • To enhance the effectiveness of machine learning models for intrusion detection through improved handling of imbalanced data.

Main Methods:

  • Employed the Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic data for rare classes.
  • Developed a numerical model using Support Vector Regression to optimize SMOTE ratios for U2R, R2L, and Probe classes.
  • Validated the optimized SMOTE ratios through experiments using various machine learning techniques on the KDD CUP 1999 dataset.

Main Results:

  • The proposed method for optimizing SMOTE ratios resulted in significantly improved performance compared to previous approaches.
  • Effective mitigation of class imbalance was achieved, leading to better detection rates for rare intrusion types.
  • The optimized SMOTE ratios enhanced the overall accuracy and reliability of the intrusion detection models.

Conclusions:

  • Optimizing SMOTE ratios using Support Vector Regression is an effective strategy for handling class imbalance in intrusion detection datasets.
  • The proposed method offers a significant advancement in building more robust and accurate network security systems.
  • This research provides a valuable contribution to the field of data mining and machine learning for cybersecurity applications.