An intrusion detection algorithm for sensor network based on normalized cut spectral clustering.

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ID: 88126
2019
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Abstract
Sensor network intrusion detection has attracted extensive attention. However, previous intrusion detection methods face the highly imbalanced attack class distribution problem, and they may not achieve a satisfactory performance. To solve this problem, we propose a new intrusion detection algorithm based on normalized cut spectral clustering for sensor network in this paper. The main aim is to reduce the imbalance degree among classes in an intrusion detection system. First, we design a normalized cut spectral clustering to reduce the imbalance degree between every two classes in the intrusion detection data set. Second, we train a network intrusion detection classifier on the new data set. Finally, we do extensive experiments and analyze the experimental results in detail. Simulation experiments show that our algorithm can reduce the imbalance degree among classes and reserves the distribution of the original data on the one hand, and improve effectively the detection performance on the other hand.
Reference Key
yang2019anplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yang, Gaoming;Yu, Xu;Xu, Lingwei;Xin, Yu;Fang, Xianjin;
Journal PloS one
Year 2019
DOI
10.1371/journal.pone.0221920
URL
Keywords
Wireless sensor networks genetic algorithm intrusion detection system wsn-ds extreme gradient boosting classifier

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