On the combination of adaptive neuro-fuzzy inference system and deep residual network for improving detection rates on intrusion detection
Deep Residual Networks (ResNets) are prone to overfitting in problems with uncertainty, such as intrusion detection problems. To alleviate this problem, we proposed a method that combines the Adaptive Neuro-fuzzy Inference System (ANFIS) and the ResNet algorithm. This method can make use of the a...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
PLOS
2022
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/40875/1/PLOS%20ONE.pdf http://ir.unimas.my/id/eprint/40875/ https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0278819 https://doi.org/10.1371/journal.pone.0278819 |
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| Summary: | Deep Residual Networks (ResNets) are prone to overfitting in problems with
uncertainty, such as intrusion detection problems. To alleviate this problem, we
proposed a method that combines the Adaptive Neuro-fuzzy Inference System
(ANFIS) and the ResNet algorithm. This method can make use of the advantages
of both the ANFIS and ResNet, and alleviate the overfitting problem of ResNet.
Compared with the original ResNet algorithm, the proposed method provides
overlapped intervals of continuous attributes and fuzzy rules to ResNet, improving
the fuzziness of ResNet. To evaluate the performance of the proposed method, the
proposed method is realized and evaluated on the benchmark NSL-KDD dataset.
Also, the performance of the proposed method is compared with the original
ResNet algorithm and other deep learning-based and ANFIS-based methods. The
experimental results demonstrate that the proposed method is better than that of the
original ResNet and other existing methods on various metrics, reaching a 98.88%
detection rate and 1.11% false alarm rate on the KDDTrain+ dataset |
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