A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection
For generating an interpretable deep architecture for identifying deep intrusion patterns, this study proposes an approach that combines ANFIS (Adaptive Network-based Fuzzy Inference System) and DT (Decision Tree) for interpreting the deep pattern of intrusion detection. Meanwhile, for improving the...
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my.unimas.ir.408382022-12-15T04:22:09Z http://ir.unimas.my/id/eprint/40838/ A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection Jia, Lu Yin Chai, Wang Chee Siong, Teh Xinjin, Li Liping, Zhao Fengrui, Wei QA75 Electronic computers. Computer science For generating an interpretable deep architecture for identifying deep intrusion patterns, this study proposes an approach that combines ANFIS (Adaptive Network-based Fuzzy Inference System) and DT (Decision Tree) for interpreting the deep pattern of intrusion detection. Meanwhile, for improving the efciency of training and predicting, Pearson Correlation analysis, standard deviation, and a new adaptive K-means are used to select attributes and make fuzzy interval decisions. The proposed algorithm was trained, validated, and tested on the NSL-KDD (National security lab–knowledge discovery and data mining) dataset. Using 22 attributes that highly related to the target, the performance of the proposed method achieves a 99.86% detection rate and 0.14% false alarm rate on the KDDTrain+dataset, a 77.46% detection rate on the KDDTest+dataset, which is better than many classifers. Besides, the interpretable model can help us demonstrate the complex and overlapped pattern of intrusions and analyze the pattern of various intrusions. Springer Nature Limited 2022 Article PeerReviewed text en http://ir.unimas.my/id/eprint/40838/1/A%20hybrid...pdf Jia, Lu and Yin Chai, Wang and Chee Siong, Teh and Xinjin, Li and Liping, Zhao and Fengrui, Wei (2022) A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection. Scientific Reports, 12 (20770). ISSN 2045-2322 https://www.nature.com/articles/s41598-022-23765-x https://doi.org/10.1038/s41598-022-23765-x |
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QA75 Electronic computers. Computer science Jia, Lu Yin Chai, Wang Chee Siong, Teh Xinjin, Li Liping, Zhao Fengrui, Wei A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection |
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For generating an interpretable deep architecture for identifying deep intrusion patterns, this study proposes an approach that combines ANFIS (Adaptive Network-based Fuzzy Inference System) and DT (Decision Tree) for interpreting the deep pattern of intrusion detection. Meanwhile, for improving the efciency of training and predicting, Pearson Correlation analysis, standard deviation, and a new adaptive K-means are used to select attributes and make fuzzy interval decisions. The proposed algorithm was trained, validated, and tested on the NSL-KDD (National security lab–knowledge discovery and data mining) dataset. Using 22 attributes that highly related to the target, the
performance of the proposed method achieves a 99.86% detection rate and 0.14% false alarm rate on the KDDTrain+dataset, a 77.46% detection rate on the KDDTest+dataset, which is better than many classifers. Besides, the interpretable model can help us demonstrate the complex and overlapped pattern of intrusions and analyze the pattern of various intrusions. |
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Article |
author |
Jia, Lu Yin Chai, Wang Chee Siong, Teh Xinjin, Li Liping, Zhao Fengrui, Wei |
author_facet |
Jia, Lu Yin Chai, Wang Chee Siong, Teh Xinjin, Li Liping, Zhao Fengrui, Wei |
author_sort |
Jia, Lu |
title |
A hybrid interpretable deep structure based on adaptive
neuro‑fuzzy inference system, decision tree, and K‑means
for intrusion detection |
title_short |
A hybrid interpretable deep structure based on adaptive
neuro‑fuzzy inference system, decision tree, and K‑means
for intrusion detection |
title_full |
A hybrid interpretable deep structure based on adaptive
neuro‑fuzzy inference system, decision tree, and K‑means
for intrusion detection |
title_fullStr |
A hybrid interpretable deep structure based on adaptive
neuro‑fuzzy inference system, decision tree, and K‑means
for intrusion detection |
title_full_unstemmed |
A hybrid interpretable deep structure based on adaptive
neuro‑fuzzy inference system, decision tree, and K‑means
for intrusion detection |
title_sort |
hybrid interpretable deep structure based on adaptive
neuro‑fuzzy inference system, decision tree, and k‑means
for intrusion detection |
publisher |
Springer Nature Limited |
publishDate |
2022 |
url |
http://ir.unimas.my/id/eprint/40838/1/A%20hybrid...pdf http://ir.unimas.my/id/eprint/40838/ https://www.nature.com/articles/s41598-022-23765-x https://doi.org/10.1038/s41598-022-23765-x |
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1753792650529472512 |
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13.18916 |