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An Open WSN Security Detection Method for Public Areas Based on Extended Bayes Classification.

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Název: An Open WSN Security Detection Method for Public Areas Based on Extended Bayes Classification.
Autoři: Zhang, Yanling1 (AUTHOR) jzdxzyl2019@163.com, Jiao, Mengtian1 (AUTHOR) jzdxjmt@163.com
Zdroj: International Journal of High Speed Electronics & Systems. Dec2025, Vol. 34 Issue 4, p1-23. 23p.
Témata: PUBLIC spaces, WIRELESS sensor networks, ANOMALY detection (Computer security), FEATURE extraction, SIGNAL-to-noise ratio
Abstrakt: There are a large number of different types of wireless signals in public areas, which are interwoven and easy to interfere with the signals of open wireless sensor network (WSN). While WSN allows different devices to access, when malicious attackers adopt camouflage technology, it is difficult to distinguish legitimate access from illegal access. Therefore, an open WSN security detection method for public areas based on extended Bayes classification is proposed. By orderly management of device access, real-time response to security threats, and scalable design of a security detection framework, open WSNs are protected from malicious attacks in public areas. By adopting technology such as feature extraction, data transformation, and anomaly detection, the network can improve its defense capability against malicious access and data tampering. In order to adapt to the dynamic changes of WSN, we consider the complex relationship between attributes and high-dimensional feature space mapping. An extended Bayes classifier is used to realize the attribute classification of WSN feature data and improve the accuracy of security detection. In the process of optimized extended Bayes classification, the built-in anti-interference mechanism improves the signal-to-noise ratio, accelerates data processing, and ensures the accuracy and real-time open WSN security detection in public areas. The experimental results show that the optimized extended Bayesian classification model can effectively distinguish between normal and malicious devices, with high accuracy, real-time performance, and stability, and is suitable for open WSN security detection in public areas. [ABSTRACT FROM AUTHOR]
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Databáze: Business Source Index
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Abstrakt:There are a large number of different types of wireless signals in public areas, which are interwoven and easy to interfere with the signals of open wireless sensor network (WSN). While WSN allows different devices to access, when malicious attackers adopt camouflage technology, it is difficult to distinguish legitimate access from illegal access. Therefore, an open WSN security detection method for public areas based on extended Bayes classification is proposed. By orderly management of device access, real-time response to security threats, and scalable design of a security detection framework, open WSNs are protected from malicious attacks in public areas. By adopting technology such as feature extraction, data transformation, and anomaly detection, the network can improve its defense capability against malicious access and data tampering. In order to adapt to the dynamic changes of WSN, we consider the complex relationship between attributes and high-dimensional feature space mapping. An extended Bayes classifier is used to realize the attribute classification of WSN feature data and improve the accuracy of security detection. In the process of optimized extended Bayes classification, the built-in anti-interference mechanism improves the signal-to-noise ratio, accelerates data processing, and ensures the accuracy and real-time open WSN security detection in public areas. The experimental results show that the optimized extended Bayesian classification model can effectively distinguish between normal and malicious devices, with high accuracy, real-time performance, and stability, and is suitable for open WSN security detection in public areas. [ABSTRACT FROM AUTHOR]
ISSN:01291564
DOI:10.1142/S0129156425403444