A Hybrid Model of Intrusion Detection Based on LMDR and CNN

doi:10.3969/j.issn.1671-1122.2020.09.024

Abstract

Abstract:

With the rapid development of network security technology and the big data technology, the traditional machine learning model has been difficult to meet the requirements of efficient intrusion detection in big data environment. For this reason, considering the advantages of convolutional neural network in feature extraction and data analysis, this paper proposed a mixed intrusion detection model based on logarithm marginal density ratio and convolutional neural network in view of the fact that the characteristics of the original dataset was not obvious enough. Compared with the traditional machine learning algorithm and neural network model, our hybrid model can make full use of the relationship between features for feature enhancement, and effectively improve the classification accuracy and reduce the false alarm rate.

Key words: intrusion detection, logarithm marginal density ratio, convotional neural network, data mining

CLC Number:

TP309

LI Qiao, LONG Chun, WEI Jinxia, ZHAO Jing. A Hybrid Model of Intrusion Detection Based on LMDR and CNN[J]. Netinfo Security, 2020, 20(9): 117-121.

Figures/Tables 4

References 14

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	Accuracy/%	DR/%	FAR/%
SVM	96.75	92.97	1.48
随机森林	99.06	98.76	0.45
朴素贝叶斯	93.35	90.29	2.58
LMDRT-SVM	99.31	99.20	0.6
LMDR-CNN(9*9)	99.49	99.41	0.39

	Accuracy/%	DR/%	FAR/%
CNN(1×41)	98.07	97.79	1.70
CNN(RF,9×9)	99.03	98.98	0.73
LMDR-CNN(1×41)	99.09	98.99	0.45
LMDR-CNN(RF,9×9)	99.49	99.41	0.39

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