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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Main Author: Wang, Ying
Other Authors: Sourav Sen Gupta
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
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Online Access:https://hdl.handle.net/10356/148081
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Wang, Ying
Privacy-preserving federated learning (I)
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.
author2 Sourav Sen Gupta
author_facet Sourav Sen Gupta
Wang, Ying
format Final Year Project
author Wang, Ying
author_sort Wang, Ying
title Privacy-preserving federated learning (I)
title_short 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_sort privacy-preserving federated learning (i)
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/148081
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