A privacy-preserving federated graph learning framework for threat detection in IoT trigger-action programming
Trigger-Action Programming (TAP) is a common user-programming paradigm in Internet of Things (IoT) smart home platforms, allowing users to create customized automation rules to match IoT devices and network services. However, the potential security threats associated with TAP rules are often overloo...
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| Published in: | Expert systems with applications Vol. 255; p. 124724 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.12.2024
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| ISSN: | 0957-4174 |
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| Abstract | Trigger-Action Programming (TAP) is a common user-programming paradigm in Internet of Things (IoT) smart home platforms, allowing users to create customized automation rules to match IoT devices and network services. However, the potential security threats associated with TAP rules are often overlooked or underestimated by users. To address this issue, we propose PFTAP, a novel federated graph learning framework for threat detection of TAP rules while simultaneously protecting user data and privacy. First, we propose a hierarchical graph attention network. This network comprises intra-rule attention and inter-rule attention modules, which enable the learning of comprehensive feature representations for triggers and actions. By capturing the intricate relationships between different rules, the network enhances the detection accuracy of risky TAP rules. Moreover, our framework is based on federated learning and integrates symmetric encryption and local differential privacy techniques, aiming to safeguard user privacy from unauthorized access or tampering. To evaluate the effectiveness of our framework, we conduct experiments using an extensive dataset of IFTTT rules. The experimental results convincingly demonstrate that PFTAP outperforms state-of-the-art methods in terms of threat detection performance.
•Propose PFTAP framework to detect the threats in TAP rules in IoT.•Propose HieGAN to learn the feature representation of triggers and actions.•Propose to use symmetric encryption and LDP to protect user privacy.•Achieve superior performance in TAP rule threat detection. |
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| AbstractList | Trigger-Action Programming (TAP) is a common user-programming paradigm in Internet of Things (IoT) smart home platforms, allowing users to create customized automation rules to match IoT devices and network services. However, the potential security threats associated with TAP rules are often overlooked or underestimated by users. To address this issue, we propose PFTAP, a novel federated graph learning framework for threat detection of TAP rules while simultaneously protecting user data and privacy. First, we propose a hierarchical graph attention network. This network comprises intra-rule attention and inter-rule attention modules, which enable the learning of comprehensive feature representations for triggers and actions. By capturing the intricate relationships between different rules, the network enhances the detection accuracy of risky TAP rules. Moreover, our framework is based on federated learning and integrates symmetric encryption and local differential privacy techniques, aiming to safeguard user privacy from unauthorized access or tampering. To evaluate the effectiveness of our framework, we conduct experiments using an extensive dataset of IFTTT rules. The experimental results convincingly demonstrate that PFTAP outperforms state-of-the-art methods in terms of threat detection performance.
•Propose PFTAP framework to detect the threats in TAP rules in IoT.•Propose HieGAN to learn the feature representation of triggers and actions.•Propose to use symmetric encryption and LDP to protect user privacy.•Achieve superior performance in TAP rule threat detection. |
| ArticleNumber | 124724 |
| Author | Hu, Liang Hu, Juncheng Xing, Yongheng Du, Xinqi Shen, Zhiqi Wang, Feng |
| Author_xml | – sequence: 1 givenname: Yongheng orcidid: 0000-0003-4980-1813 surname: Xing fullname: Xing, Yongheng email: xingyh18@mails.jlu.edu.cn organization: College of Computer Science and Technology, Jilin University, Changchun 130012, China – sequence: 2 givenname: Liang surname: Hu fullname: Hu, Liang email: hul@jlu.edu.cn organization: College of Computer Science and Technology, Jilin University, Changchun 130012, China – sequence: 3 givenname: Xinqi surname: Du fullname: Du, Xinqi email: duxq18@mails.jlu.edu.cn organization: School of Control Science and Engineering, Dalian University of Technology, Dalian 116081, China – sequence: 4 givenname: Zhiqi surname: Shen fullname: Shen, Zhiqi email: zqshen@ntu.edu.sg organization: Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) & School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore – sequence: 5 givenname: Juncheng surname: Hu fullname: Hu, Juncheng email: jchu@jlu.edu.cn organization: College of Computer Science and Technology, Jilin University, Changchun 130012, China – sequence: 6 givenname: Feng orcidid: 0000-0002-0732-7343 surname: Wang fullname: Wang, Feng email: wangfeng12@mails.jlu.edu.cn organization: College of Computer Science and Technology, Jilin University, Changchun 130012, China |
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| Cites_doi | 10.1016/j.eswa.2023.121065 10.1109/TIFS.2020.2988575 10.1109/TIFS.2019.2899758 10.1145/3576842.3582328 10.1109/JIOT.2022.3222615 10.6028/jres.106.023 10.1109/TII.2021.3092774 10.1016/j.cose.2022.102812 10.1145/3038912.3052709 10.1145/3319535.3345662 10.1145/3131365.3131369 10.1109/TIFS.2022.3174394 10.1109/TIFS.2022.3214084 10.1109/MIS.2020.3014880 10.1109/JIOT.2020.3019812 10.1145/3543507.3583293 10.1145/3298981 |
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| Keywords | Privacy protection Trigger-action programming Federated learning Rule threat detection Graph attention network |
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| SubjectTerms | Federated learning Graph attention network Privacy protection Rule threat detection Trigger-action programming |
| Title | A privacy-preserving federated graph learning framework for threat detection in IoT trigger-action programming |
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