Method on intrusion detection for industrial internet based on light gradient boosting machine
Intrusion detection is a critical security protection technology in the industrial internet, and it plays a vital role in ensuring the security of the system.In order to meet the requirements of high accuracy and high real-time intrusion detection in industrial internet, an industrial internet intru...
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POSTS&TELECOM PRESS Co., LTD
2023-04-01
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| Series: | 网络与信息安全学报 |
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| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023020 |
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| _version_ | 1841529733137825792 |
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| author | Xiangdong HU Lingling TANG |
| author_facet | Xiangdong HU Lingling TANG |
| author_sort | Xiangdong HU |
| collection | DOAJ |
| description | Intrusion detection is a critical security protection technology in the industrial internet, and it plays a vital role in ensuring the security of the system.In order to meet the requirements of high accuracy and high real-time intrusion detection in industrial internet, an industrial internet intrusion detection method based on light gradient boosting machine optimization was proposed.To address the problem of low detection accuracy caused by difficult-to-classify samples in industrial internet business data, the original loss function of the light gradient boosting machine as a focal loss function was improved.This function can dynamically adjust the loss value and weight of different types of data samples during the training process, reducing the weight of easy-to-classify samples to improve detection accuracy for difficult-to-classify samples.Then a fruit fly optimization algorithm was used to select the optimal parameter combination of the model for the problem that the light gradient boosting machine has many parameters and has great influence on the detection accuracy, detection time and fitting degree of the model.Finally, the optimal parameter combination of the model was obtained and verified on the gas pipeline dataset provided by Mississippi State University, then the effectiveness of the proposed mode was further verified on the water dataset.The experimental results show that the proposed method achieves higher detection accuracy and lower detection time than the comparison model.The detection accuracy of the proposed method on the gas pipeline dataset is at least 3.14% higher than that of the comparison model.The detection time is 0.35s and 19.53s lower than that of the random forest and support vector machine in the comparison model, and 0.06s and 0.02s higher than that of the decision tree and extreme gradient boosting machine, respectively.The proposed method also achieved good detection results on the water dataset.Therefore, the proposed method can effectively identify attack data samples in industrial internet business data and improve the practicality and efficiency of intrusion detection in the industrial internet. |
| format | Article |
| id | doaj-art-b64deffe3462409281b216e9aa45e99b |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2023-04-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-b64deffe3462409281b216e9aa45e99b2025-01-15T03:16:16ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2023-04-019465559575926Method on intrusion detection for industrial internet based on light gradient boosting machineXiangdong HULingling TANGIntrusion detection is a critical security protection technology in the industrial internet, and it plays a vital role in ensuring the security of the system.In order to meet the requirements of high accuracy and high real-time intrusion detection in industrial internet, an industrial internet intrusion detection method based on light gradient boosting machine optimization was proposed.To address the problem of low detection accuracy caused by difficult-to-classify samples in industrial internet business data, the original loss function of the light gradient boosting machine as a focal loss function was improved.This function can dynamically adjust the loss value and weight of different types of data samples during the training process, reducing the weight of easy-to-classify samples to improve detection accuracy for difficult-to-classify samples.Then a fruit fly optimization algorithm was used to select the optimal parameter combination of the model for the problem that the light gradient boosting machine has many parameters and has great influence on the detection accuracy, detection time and fitting degree of the model.Finally, the optimal parameter combination of the model was obtained and verified on the gas pipeline dataset provided by Mississippi State University, then the effectiveness of the proposed mode was further verified on the water dataset.The experimental results show that the proposed method achieves higher detection accuracy and lower detection time than the comparison model.The detection accuracy of the proposed method on the gas pipeline dataset is at least 3.14% higher than that of the comparison model.The detection time is 0.35s and 19.53s lower than that of the random forest and support vector machine in the comparison model, and 0.06s and 0.02s higher than that of the decision tree and extreme gradient boosting machine, respectively.The proposed method also achieved good detection results on the water dataset.Therefore, the proposed method can effectively identify attack data samples in industrial internet business data and improve the practicality and efficiency of intrusion detection in the industrial internet.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023020industrial internetintrusion detectionlight gradient boosting machinefocal lossfruit fly optimization algorithm |
| spellingShingle | Xiangdong HU Lingling TANG Method on intrusion detection for industrial internet based on light gradient boosting machine 网络与信息安全学报 industrial internet intrusion detection light gradient boosting machine focal loss fruit fly optimization algorithm |
| title | Method on intrusion detection for industrial internet based on light gradient boosting machine |
| title_full | Method on intrusion detection for industrial internet based on light gradient boosting machine |
| title_fullStr | Method on intrusion detection for industrial internet based on light gradient boosting machine |
| title_full_unstemmed | Method on intrusion detection for industrial internet based on light gradient boosting machine |
| title_short | Method on intrusion detection for industrial internet based on light gradient boosting machine |
| title_sort | method on intrusion detection for industrial internet based on light gradient boosting machine |
| topic | industrial internet intrusion detection light gradient boosting machine focal loss fruit fly optimization algorithm |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023020 |
| work_keys_str_mv | AT xiangdonghu methodonintrusiondetectionforindustrialinternetbasedonlightgradientboostingmachine AT linglingtang methodonintrusiondetectionforindustrialinternetbasedonlightgradientboostingmachine |