A Study on Distributed Learning for Attack Detection in Wireless Sensor Networks
Wireless Sensor Networks (WSNs) have become a cornerstone in the implementation of Structural Health Monitoring (SHM) systems due to their versatility, energy efficiency, and adaptability. Their ability to facilitate low-powered communication makes them ideal for monitoring the structural integrity...
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| Vydané v: | Prognostics and System Health Management Conference s. 1 - 8 |
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| Hlavní autori: | , , |
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| Jazyk: | English |
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IEEE
09.06.2025
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| ISSN: | 2166-5656 |
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| Abstract | Wireless Sensor Networks (WSNs) have become a cornerstone in the implementation of Structural Health Monitoring (SHM) systems due to their versatility, energy efficiency, and adaptability. Their ability to facilitate low-powered communication makes them ideal for monitoring the structural integrity of environments where traditional wired connections are impractical. This paper explores the integration of a computer cluster with a WSN to enhance the functionality of an SHM system. Specifically, we investigate the feasibility of using a cluster of four Raspberry Pi computers to assess the feasibility of using distributed models in PyTorch and TensorFlow, assessing their applicability in heterogeneous network environments. Additionally, we examine the differences between machine learning-based and non-machine learning-based security approaches for WSNs. We analyze existing attack mitigation strategies, including traditional rule-based intrusion detection systems and cryptographic techniques, alongside ML-driven anomaly detection, classification, and threat prediction models. By evaluating how a computer cluster can enhance these security mechanisms, we aim to provide insights into the effectiveness of ML and non-ML-based techniques in improving attack detection, prevention, and overall system resilience in SHM deployments. |
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| AbstractList | Wireless Sensor Networks (WSNs) have become a cornerstone in the implementation of Structural Health Monitoring (SHM) systems due to their versatility, energy efficiency, and adaptability. Their ability to facilitate low-powered communication makes them ideal for monitoring the structural integrity of environments where traditional wired connections are impractical. This paper explores the integration of a computer cluster with a WSN to enhance the functionality of an SHM system. Specifically, we investigate the feasibility of using a cluster of four Raspberry Pi computers to assess the feasibility of using distributed models in PyTorch and TensorFlow, assessing their applicability in heterogeneous network environments. Additionally, we examine the differences between machine learning-based and non-machine learning-based security approaches for WSNs. We analyze existing attack mitigation strategies, including traditional rule-based intrusion detection systems and cryptographic techniques, alongside ML-driven anomaly detection, classification, and threat prediction models. By evaluating how a computer cluster can enhance these security mechanisms, we aim to provide insights into the effectiveness of ML and non-ML-based techniques in improving attack detection, prevention, and overall system resilience in SHM deployments. |
| Author | Zhou, Ruolin Popillo, Cameron Savage, Eric Noah |
| Author_xml | – sequence: 1 givenname: Eric Noah surname: Savage fullname: Savage, Eric Noah email: esavage@umassd.edu organization: University of Massachusetts,Department of Electrical and Computer Engineering,Dartmouth,MA – sequence: 2 givenname: Cameron surname: Popillo fullname: Popillo, Cameron email: cpopillo@umassd.edu organization: University of Massachusetts,Department of Electrical and Computer Engineering,Dartmouth,MA – sequence: 3 givenname: Ruolin surname: Zhou fullname: Zhou, Ruolin email: rzhou@umassd.edu organization: University of Massachusetts,Department of Electrical and Computer Engineering,Dartmouth,MA |
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| Snippet | Wireless Sensor Networks (WSNs) have become a cornerstone in the implementation of Structural Health Monitoring (SHM) systems due to their versatility, energy... |
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| SubjectTerms | Computational modeling Computer hacking Fault tolerance Heterogeneous networks Intrusion detection Monitoring Predictive models Prevention and mitigation Prognostics and health management Wireless sensor networks |
| Title | A Study on Distributed Learning for Attack Detection in Wireless Sensor Networks |
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