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A novel intrusion detection method based on threshold modification using receiver operating characteristic curve
Author(s) -
Luo Jun,
Chai Senchun,
Zhang Baihai,
Xia Yuanqing,
Gao Jianlei,
Zeng Guoqiang
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5690
Subject(s) - receiver operating characteristic , intrusion detection system , classifier (uml) , computer science , convolutional neural network , pattern recognition (psychology) , false positive rate , artificial intelligence , intrusion , artificial neural network , algorithm , machine learning , geochemistry , geology
Summary Class imbalance makes traditional intrusion detection system have low detection rate (DR) and high false positive rate (FR) for minority class, which is unsuitable for practical needs. In order to improve the DRs and reduce FRs of minority classes, we propose a novel intrusion detection method, which combines convolutional neural networks (CNNs) algorithm with threshold modification method based on receiver operating characteristic (ROC) curve. In this method, we use CNNs as a classifier and modify threshold of the classifier through ROC curve. In addition, NSLKDD dataset and UNSW‐NB15 dataset have been carried out to evaluate the performance of this method. The experimental results illustrate that the proposed method has a better performance no matter in improving DRs or reducing FRs of minority classes.