Privacy-preserving federated learning (I)
Botnet attack is a critical problem in IoT devices. However, current botnet detection technology like the autoencoder model requires centralized training on a large amount of data collected from IoT devices from different home networks, which is not practical because the sensitive information in the...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/148081 |
| _version_ | 1811684613603459072 |
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| author | Wang, Ying |
| author2 | Sourav Sen Gupta |
| author_facet | Sourav Sen Gupta Wang, Ying |
| author_sort | Wang, Ying |
| collection | NTU |
| description | Botnet attack is a critical problem in IoT devices. However, current botnet detection technology like the autoencoder model requires centralized training on a large amount of data collected from IoT devices from different home networks, which is not practical because the sensitive information in these data may cause privacy leakage. Individual Learning within the home network also can diminish the performance of the autoencoder. Federated Learning that allows the home networks to learn a shared model with data kept locally is a solution. In this project, we implement the Federated Learning approach on autoencoder for botnet detection and compare Federated Learning's performance with Centralized Training and Individual Learning in terms of prediction accuracy. The Federated Training approach shows a noticeable improvement in prediction accuracy over Individual Learning and a similar performance compared with Centralized Learning. In addition, to further reduce the chance that the model being attacked, the Federated Learning approach is further transformed to a secure aggregation platform. The prediction result of Federated Learning with and without secure aggregation shows that the secure aggregation platform doesn’t affect the performance of Federated Learning. |
| first_indexed | 2024-10-01T04:31:25Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/148081 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:31:25Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1480812021-04-22T13:08:52Z Privacy-preserving federated learning (I) Wang, Ying Sourav Sen Gupta School of Computer Science and Engineering sg.sourav@ntu.edu.sg Engineering::Computer science and engineering Botnet attack is a critical problem in IoT devices. However, current botnet detection technology like the autoencoder model requires centralized training on a large amount of data collected from IoT devices from different home networks, which is not practical because the sensitive information in these data may cause privacy leakage. Individual Learning within the home network also can diminish the performance of the autoencoder. Federated Learning that allows the home networks to learn a shared model with data kept locally is a solution. In this project, we implement the Federated Learning approach on autoencoder for botnet detection and compare Federated Learning's performance with Centralized Training and Individual Learning in terms of prediction accuracy. The Federated Training approach shows a noticeable improvement in prediction accuracy over Individual Learning and a similar performance compared with Centralized Learning. In addition, to further reduce the chance that the model being attacked, the Federated Learning approach is further transformed to a secure aggregation platform. The prediction result of Federated Learning with and without secure aggregation shows that the secure aggregation platform doesn’t affect the performance of Federated Learning. Bachelor of Engineering (Computer Engineering) 2021-04-22T13:08:52Z 2021-04-22T13:08:52Z 2021 Final Year Project (FYP) Wang, Y. (2021). Privacy-preserving federated learning (I). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148081 https://hdl.handle.net/10356/148081 en SCSE20-0544 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Wang, Ying Privacy-preserving federated learning (I) |
| title | Privacy-preserving federated learning (I) |
| title_full | Privacy-preserving federated learning (I) |
| title_fullStr | Privacy-preserving federated learning (I) |
| title_full_unstemmed | Privacy-preserving federated learning (I) |
| title_short | Privacy-preserving federated learning (I) |
| title_sort | privacy preserving federated learning i |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/148081 |
| work_keys_str_mv | AT wangying privacypreservingfederatedlearningi |