Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 705-712.doi: 10.11896/jsjkx.201100101

• Interdiscipline & Application • Previous Articles     Next Articles

Design of Intrusion Detection System Based on Sampling Ensemble Algorithm

HUAN Wen-ming, LIN Hai-tao   

  1. College of Electronics Engineering,Naval University of Engineering,Wuhan 430000,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:HUAN Wen-ming,born in 1995,postgraduate.His main research interests include data mining and cyber security.
    LIN Hai-tao,born in 1974,Ph.D,associate professor.His main research inte-rests include information network mana-gement and planning.

Abstract: As the second line of defense after firewalls,intrusion detection systems have been widely used in the field of network security.Machine learning-based intrusion detection systems have attracted more and more interest due to their superior detection performance.In order to improve the detection performance of the intrusion detection system in multiple types of imbalanced data,this paper proposes an intrusion detection system based on the optimal sampling ensemble algorithm(OSEC).OSEC first converts the multi-category detection problem into multiple binary classification problems according to the “one-to-all” principle,and then selects the optimal sampling ensemble algorithm according to the AUC value in each binary classification problem to alleviate the data imbalance problem.Finally,the category judgment module designed in this article judges the specific category of the sample to be tested.We perform simulation verification on the NSL-KDD data set,and find that compared with the traditional method,the F1 score of this system on R2L and U2R has increased by 0.595 and 0.185 respectively;compared with the latest intrusion detection system,the method in this paper improves the overall detection accuracy by 1.4%.

Key words: AUC, Ensemble learning, Intrusion detection, Multi-class imbalanced, NSL-KDD, Resampling

CLC Number: 

  • TP309
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