Secure federated distillation GAN for CIDS in industrial CPS
Aiming at the data island problem caused by the imperativeness of confidentiality of sensitive information, a secure and collaborative intrusion detection system (CIDS) for industrial cyber physical systems (CPS) was proposed, called PFD-GAN.Specifically, a novel semi-supervised intrusion detection...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2023-12-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023216/ |
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| _version_ | 1841540057646759936 |
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| author | Junwei LIANG Geng YANG Maode MA Sadiq Muhammad |
| author_facet | Junwei LIANG Geng YANG Maode MA Sadiq Muhammad |
| author_sort | Junwei LIANG |
| collection | DOAJ |
| description | Aiming at the data island problem caused by the imperativeness of confidentiality of sensitive information, a secure and collaborative intrusion detection system (CIDS) for industrial cyber physical systems (CPS) was proposed, called PFD-GAN.Specifically, a novel semi-supervised intrusion detection model was firstly developed by improving external classifier-generative adversarial network (EC-GAN) with Wasserstein distance and label condition, to strengthen the classification performance through the use of synthetic data.Furthermore, local differential privacy (LDP) technology was incorporated into the training process of developed EC-GAN to prevent sensitive information leakage and ensure privacy and security in collaboration.Moreover, a decentralized federated distillation (DFD)-based collaboration was designed, allowing multiple industrial CPS to collectively build a comprehensive intrusion detection system (IDS) to recognize the threats under the entire cyber systems without sharing a uniform template model.Experimental evaluation and theory analysis demonstrate that the proposed PFD-GAN is secure from the threats of privacy leaking and highly effective in detecting various types of attacks on industrial CPS. |
| format | Article |
| id | doaj-art-94f9203db21c4c7eb8b8492f13371010 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-12-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-94f9203db21c4c7eb8b8492f133710102025-01-14T06:22:33ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-12-014423024459384910Secure federated distillation GAN for CIDS in industrial CPSJunwei LIANGGeng YANGMaode MASadiq MuhammadAiming at the data island problem caused by the imperativeness of confidentiality of sensitive information, a secure and collaborative intrusion detection system (CIDS) for industrial cyber physical systems (CPS) was proposed, called PFD-GAN.Specifically, a novel semi-supervised intrusion detection model was firstly developed by improving external classifier-generative adversarial network (EC-GAN) with Wasserstein distance and label condition, to strengthen the classification performance through the use of synthetic data.Furthermore, local differential privacy (LDP) technology was incorporated into the training process of developed EC-GAN to prevent sensitive information leakage and ensure privacy and security in collaboration.Moreover, a decentralized federated distillation (DFD)-based collaboration was designed, allowing multiple industrial CPS to collectively build a comprehensive intrusion detection system (IDS) to recognize the threats under the entire cyber systems without sharing a uniform template model.Experimental evaluation and theory analysis demonstrate that the proposed PFD-GAN is secure from the threats of privacy leaking and highly effective in detecting various types of attacks on industrial CPS.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023216/intrusion detection systemcyber physical systemgenerative adversarial networklocal differential privacydecentralized federated distillation |
| spellingShingle | Junwei LIANG Geng YANG Maode MA Sadiq Muhammad Secure federated distillation GAN for CIDS in industrial CPS Tongxin xuebao intrusion detection system cyber physical system generative adversarial network local differential privacy decentralized federated distillation |
| title | Secure federated distillation GAN for CIDS in industrial CPS |
| title_full | Secure federated distillation GAN for CIDS in industrial CPS |
| title_fullStr | Secure federated distillation GAN for CIDS in industrial CPS |
| title_full_unstemmed | Secure federated distillation GAN for CIDS in industrial CPS |
| title_short | Secure federated distillation GAN for CIDS in industrial CPS |
| title_sort | secure federated distillation gan for cids in industrial cps |
| topic | intrusion detection system cyber physical system generative adversarial network local differential privacy decentralized federated distillation |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023216/ |
| work_keys_str_mv | AT junweiliang securefederateddistillationganforcidsinindustrialcps AT gengyang securefederateddistillationganforcidsinindustrialcps AT maodema securefederateddistillationganforcidsinindustrialcps AT sadiqmuhammad securefederateddistillationganforcidsinindustrialcps |