Lightweight anomaly detection scheme using incremental principal component analysis and support vector machine

HIGHLIGHTS

• who: Nurfazrina M. Zamry and colleagues from the School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Iskandar Puteri, MalaysiaDepartment of Information Technology, College of Computer, Qassim University have published the article: Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine, in the Journal: Sensors 2021, 8017 of /2021/
• what: Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. In this study, a lightweight and effective anomaly detection scheme is proposed. The aim of this study is to design more energy-efficient communication in WSNs and reduce the . . .
  

   

  

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