Chinese Journal of Network and Information Security ›› 2018, Vol. 4 ›› Issue (7): 48-59.doi: 10.11959/j.issn.2096-109x.2018056
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Binghao YAN,Guodong HAN
Revised:
2018-07-05
Online:
2018-07-01
Published:
2018-09-10
Supported by:
CLC Number:
Binghao YAN,Guodong HAN. Combinatorial intrusion detection model based on deep recurrent neural network and improved SMOTE algorithm[J]. Chinese Journal of Network and Information Security, 2018, 4(7): 48-59.
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样本类别 | 评价指标 | DRNN | DRNN+SMOTE | DRNN+RASMOTE |
Normal | PRE | 98.52% | 98.66% | 99.01% |
FAR | 1.47% | 1.28% | 1.29% | |
DoS | PRE | 97.92% | 98.34% | 98.76% |
FAR | 2.27% | 2.04% | 1.97% | |
Probing | PRE | 97.88% | 98.56% | 98.42% |
FAR | 3.04% | 1.54% | 1.23% | |
R2L | PRE | 69.26% | 94.63% | 95.39% |
FAR | 32.78% | 6.67% | 3.99% | |
U2L | PRE | 24.32% | 92.54% | 94.02% |
FAR | 77.50% | 7.27% | 5.60% | |
模型训练时间Ttrain/s | 2.68 | 3.24 | 2.93 |
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