English(EN) External Sinkhole Attack Detection in Large-Scale WSNs Using Metaheuristic Feature Selection

新方法检测大规模无线传感器网络中的陷门攻击

作者 PulseAugur 编辑部 · [2 个来源] · 2026-08-15 15:16

研究人员开发了一种检测大规模无线传感器网络（WSNs）中外部陷门攻击的新方法。该方法利用了由蜂群优化（BSO）算法驱动的元启发式特征选择技术。在涉及2000个节点的模拟中，该方法成功地以0.997的准确率识别了攻击，并将初始的16个特征集减少到8个。 AI

影响该研究通过提高攻击检测能力，为大规模无线传感器网络的安全性做出了贡献。

排序理由该集群包含一篇详细介绍网络攻击检测新方法的学术论文。[lever_c_demoted from research: ic=2 ai=0.4]

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arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Su Man Nam · 2026-08-15 15:16

使用元启发式特征选择在大规模无线传感器网络中检测外部陷门攻击

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 sim…
arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Su Man Nam · 2026-08-15 15:16

基于元启发式特征选择的大规模无线传感器网络外部陷门攻击检测

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 sim…