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Secure Clone Node Detection in Wireless Sensor Networks Using Spatial Feature Clustering and Ensemble Neural Classification
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DOI：10.3390/info17050490.png)
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Abstract
En 中文
WSNs are a core technology that enables real-time sensing and data collection in most applications; however, because of the uncontrollable nature of their open deployment environments, they are susceptible to severe security risks. The node clone attacks are the most dangerous: a malicious individual physically captures a legitimate sensor and steals its stored credentials and introduces several replica nodes into the network. These clones have legitimate identities, and hence, the clones act as legitimate members and can disrupt data streams, disrupt routing and affect general network reliability. Addressing this menace is not easy since sensor equipment has limited resources. Carefully, detection algorithms have to be energy efficient, friendly to memory, and usable in a large network. In the given paper, it is suggested to implement a detection framework that consists of a combination of Spatial Distributive Clustering (SDC) and a Block Ensemble Neural Network (BENN). SDC clusters node features based on spatial layout and behavioral patterns, which minimizes redundancy of data and enhances the quality of information that is inputted by the classifier. BENN then undergoes an ensemble-based classification to be able to differentiate cloned and legitimate nodes. Validation of the experimental results of the SDC-BENN framework with conventional classification metrics indicates that it can be used to ensure a high detection rate with minimal communication overhead, which is of high benefit in terms of enhancing the security of WSNs.
Keywords:
Wireless Sensor Networks (WSNs)
clone attack classification
Spatial Distributive Clustering (SDC)
Block Ensemble Neural Network (BENN)