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

Autoři: Gaoming Yang aff001;  Xu Yu aff002;  Lingwei Xu aff002;  Yu Xin aff003;  Xianjin Fang aff001
Působiště autorů: School of Computer Science & Engineering, Anhui University of Science & Technology, Huainan, Anhui, China aff001;  Department of Information Science & Technology, Qingdao University of Science & Technology, Qingdao, Shandong, China aff002;  Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article


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.

Klíčová slova:

Algorithms – Computer networks – Data acquisition – Machine learning algorithms – Support vector machines – Wireless sensor networks – Spectral clustering – Ranking algorithms


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2019 Číslo 10
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