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This manuscript presents a thorough analysis of the ensemble model created using the Greedy Approach focusing specifically for security domain. The objective of this research is to achieve the optimal combination of the 5 base classifiers to achieve optimal performance metrics. The proposed greedy ensemble approach is simulated on the credit card dataset towards determining frauds, to validate its effectiveness and efficiency. After analysis of the obtained results, it is evident that it achieves F1 score of 0.83 which is substantially higher than 0.79 of the random approach. Thus, it can be safely concluded that the greedy approach can be utilized for devising ensemble modeling. Thus, the current research work can be considered as a contributing step towards practical security issues such as fraud detection, malware classification, and intrusion detection.

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A Greedy Hybrid Ensemble Approach for Security Applications: Fraud, Intrusion, and Malware Detection

  • 章节

  • 发表日期：

    2025年

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  • Monika Mangla,
  • Nonita Sharma,
  • Madhuchhanda Tripathy,
  • Vaishali Mehta,
  • Manik Rakhra

摘要

This manuscript presents a thorough analysis of the ensemble model created using the Greedy Approach focusing specifically for security domain. The objective of this research is to achieve the optimal combination of the 5 base classifiers to achieve optimal performance metrics. The proposed greedy ensemble approach is simulated on the credit card dataset towards determining frauds, to validate its effectiveness and efficiency. After analysis of the obtained results, it is evident that it achieves F1 score of 0.83 which is substantially higher than 0.79 of the random approach. Thus, it can be safely concluded that the greedy approach can be utilized for devising ensemble modeling. Thus, the current research work can be considered as a contributing step towards practical security issues such as fraud detection, malware classification, and intrusion detection.