Intrusion detection systems play a very important role in industrial Internet network security. However, in the large-scale, complex, and heterogeneous industrial Internet of Things (IoT), it is becoming more and more difficult to defend network intrusion threats due to the insufficiency of high-quality attack samples. To solve the problem, an efficient federated network intrusion method called EFedID is proposed for industrial IoT, which can allow different industrial agents to collaboratively train a comprehensive detection model. Specifically, the adaptive gradient sparsification method is introduced to alleviate the communication and computation overheads. To protect the data privacy of the agents, a CKKS cryptosystem-based secure communication protocol is designed to encrypt the model parameters through the federated training process. Our proposed system demonstrates exceptional detection performance on the NSL-KDD, KDD CUP 99, and CICIDS 2017 datasets. Notably, on the NSL-KDD dataset, the model compression rate reaches 9 times while the model accuracy reaches 84.31%. On the KDD CUP 99 dataset, the model compression rate reaches 8.9 times while the model accuracy reaches 97.3%. Lastly, on the CICIDS 2017 dataset, the model compression rate reached 6.173 times while the model accuracy reached 95.51%. The experimental results demonstrate that the proposed method is very suitable for effectively developing a high-accuracy detection model while protecting the data information of industrial agents. Furthermore, the method can be extended to other recent deep learning networks for intrusion detection.
{"title":"Efficient Privacy-Preserving Federated Deep Learning for Network Intrusion of Industrial IoT","authors":"Ningxin He, Zehui Zhang, Xiaotian Wang, Tiegang Gao","doi":"10.1155/2023/2956990","DOIUrl":"https://doi.org/10.1155/2023/2956990","url":null,"abstract":"Intrusion detection systems play a very important role in industrial Internet network security. However, in the large-scale, complex, and heterogeneous industrial Internet of Things (IoT), it is becoming more and more difficult to defend network intrusion threats due to the insufficiency of high-quality attack samples. To solve the problem, an efficient federated network intrusion method called EFedID is proposed for industrial IoT, which can allow different industrial agents to collaboratively train a comprehensive detection model. Specifically, the adaptive gradient sparsification method is introduced to alleviate the communication and computation overheads. To protect the data privacy of the agents, a CKKS cryptosystem-based secure communication protocol is designed to encrypt the model parameters through the federated training process. Our proposed system demonstrates exceptional detection performance on the NSL-KDD, KDD CUP 99, and CICIDS 2017 datasets. Notably, on the NSL-KDD dataset, the model compression rate reaches 9 times while the model accuracy reaches 84.31%. On the KDD CUP 99 dataset, the model compression rate reaches 8.9 times while the model accuracy reaches 97.3%. Lastly, on the CICIDS 2017 dataset, the model compression rate reached 6.173 times while the model accuracy reached 95.51%. The experimental results demonstrate that the proposed method is very suitable for effectively developing a high-accuracy detection model while protecting the data information of industrial agents. Furthermore, the method can be extended to other recent deep learning networks for intrusion detection.","PeriodicalId":507857,"journal":{"name":"International Journal of Intelligent Systems","volume":"148 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139269267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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