Cloud Computing-Based Security Analysis on Wireless Sensor Nodes Cluster Using Predictive Technique
Rapid technological advancements have led to the widespread deployment of wireless sensor networks (WSNs) in industrial environments, making cybersecurity a critical concern in cloud computing. This paper presents a predictive framework for cloud-based intrusion detection and prevention for WSNs. I...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IIUM Press, International Islamic University Malaysia
2025-05-01
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| Series: | International Islamic University Malaysia Engineering Journal |
| Subjects: | |
| Online Access: | https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3393 |
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| Summary: | Rapid technological advancements have led to the widespread deployment of wireless sensor networks (WSNs) in industrial environments, making cybersecurity a critical concern in cloud computing. This paper presents a predictive framework for cloud-based intrusion detection and prevention for WSNs. It integrates machine learning models—Multilayer Perceptron (MLP), Decision Tree, and Autoencoder—to precisely classify and mitigate various impacts of cyber intrusions on a cluster of wireless sensors. An intelligent prioritization and prevention system is also proposed, categorizing attacks—blackhole, grayhole, flooding, and scheduling—based on their impact on industrial processes. Experimental results indicate robust detection capabilities, with the Decision Tree achieving 99.48% accuracy, slightly outperforming MLP at 99.37%. The Autoencoder demonstrated superior binary classification, distinguishing between normal and anomalous instances with high precision and recall rates. This framework leverages the WSN-DS dataset to simulate and validate its efficiency in mitigating real-time threats. Future work will focus on refining the prioritization model and integrating advanced machine learning techniques for enhanced adaptability and resilience.
ABSTRAK: Kemajuan pesat dalam teknologi telah membawa kepada penggunaan meluas rangkaian penderia wayarles (WSN) dalam persekitaran industri, menjadikan keselamatan siber sebagai kebimbangan kritikal dalam pengkomputeran awan. Kajian ini membentangkan rangka kerja ramalan bagi mengesan dan mencegah pencerobohan berasaskan awan untuk WSN. Ia menyepadukan model pembelajaran mesin—Perseptron Berbilang Lapis (MLP), Pokok Keputusan (Decision Tree) dan Enkoder Automatik (Autoencoder)—bagi klasifikasi tepat dan pengurangan pelbagai kesan pencerobohan siber pada kelompok penderia wayarles. Sistem keutamaan dan pencegahan pintar turut dicadangkan, mengkategorikan serangan—lubang hitam, lubang kelabu, banjir dan penjadualan—berdasarkan kesan terhadap proses industri. Dapatan eksperimen menunjukkan keupayaan pengesanan yang mantap dengan Decision Tree mencapai ketepatan 99.48%, sedikit mengatasi prestasi MLP pada 99.37%. Autoencoder menunjukkan klasifikasi binari yang unggul, membezakan antara kejadian biasa dan anomali dengan ketepatan tinggi dan kadar ingatan semula. Rangka kerja ini memanfaatkan set data WSN-DS bagi simulasi dan pengesahan kecekapan dalam mengurangkan ancaman masa nyata. Kajian akan menumpukan pada memperhalusi model keutamaan dan menyepadukan teknik pembelajaran mesin lanjutan pada masa hadapan bagi kebolehsuaian dan daya tahan yang tinggi.
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| ISSN: | 1511-788X 2289-7860 |