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arXiv · 2608.15274

External Sinkhole Attack Detection in Large-Scale WSNs Using Metaheuristic Feature Selection

Seungwoo Han·Sawako Kitagata·Ingon Chanpornpakdi·Toshihisa Tanaka·Su Man Nam
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Abstract

Sinkhole attacks in large-scale wireless sensor networks (WSNs) pose a serious threat to network functionality. This paper presents a metaheuristic feature selection for sinkhole attack detection using the bee swarm optimization (BSO) algorithm. In an external sinkhole attack simulation with 2000 nodes deployed over a 3000 $\times$ 3000 m$^2$ field, the proposed method achieves a detection accuracy of 0.997 while reducing the 16-feature set to eight features.

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Seungwoo Han, Sawako Kitagata, Ingon Chanpornpakdi, Toshihisa Tanaka, Su Man Nam. 2026-08-15. External Sinkhole Attack Detection in Large-Scale WSNs Using Metaheuristic Feature Selection. https://arxiv.org/abs/2608.15274

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