A Hybrid Supervised and Unsupervised Learning Approach for Node Classification

A Hybrid Supervised and Unsupervised Learning Approach for Node Classification

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2016 by IJETT Journal
Volume-32 Number-4
Year of Publication : 2016
Authors : Alekhyasuma, P.Rajasekhar

Citation 

Alekhyasuma, P.Rajasekhar"A Hybrid Supervised and Unsupervised Learning Approach for Node Classification", International Journal of Engineering Trends and Technology (IJETT), V32(4),171-174 February 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Malicious node identification is always an interesting research issue in the field of network security. Various approaches like statistical, distance and density based techniques (trust measures, SVM Base approaches, classification mechanisms) introduced by the various researchers. In this paper we are proposing a novel cluster based approach for detecting the anomaly or outlier node while communicating to the destination node, by initially computing the clusters and followed by the positive and negative probabilities.

 References

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Keywords
Various approaches like statistical, distance and density based techniques.