Title
A projection pursuit based risk assessment method in mobile ad hoc networks
Abstract
Establishing high performance cooperation and estimating the nodes' risk in mobile ad hoc networks (MANETS) is currently fundamental and challenging due to the inherent characteristics of MANETS, such as highly dynamic topology and absence of an effective security mechanism. Trust based assessment methods were recently put forward but presumed restrictions to the data samples or presumed weights for node's attributes is required. In our paper, PPRA method, short for Projection Pursuit based risk assessment, is proposed to analyze node's creditability. As projection pursuit turns high-dimensional node properties to low-dimension space, all nodes' risk levels could be clustered easily and accurately. Projection index, the same to judgment index of clustering consequence, is enacted to reveal different node behaviors. By maximizing projection index through Genetic Algorithm (GA) optimal projection direction is obtained, and then every node's projection value could be calculated. We prove that relationship between projection value and node risk level is that the larger projection value is, the more credible node is. Finally, the results in one-dimension and two-dimension projection prove our method is more efficient and practical than the traditional methods for risk assessment. © 2010 IEEE.
Year
DOI
Venue
2011
10.1109/IPTC.2010.169
Proceedings - 2010 International Symposium on Intelligence Information Processing and Trusted Computing, IPTC 2010
Keywords
Field
DocType
genetic algorithm,project pursuit,projection direction,projection index,risk assessment,mobile ad hoc network,projective space,projection pursuit,indexation,two dimensions
Mobile ad hoc network,Risk level,Data mining,Mathematical optimization,Projection pursuit,Risk assessment,Artificial intelligence,Cluster analysis,Mathematics,Genetic algorithm,Machine learning
Journal
Volume
Issue
ISSN
null
null
null
ISBN
Citations 
PageRank 
978-0-7695-4196-9
3
0.39
References 
Authors
5
5
Name
Order
Citations
PageRank
fu cai130.39
Ming Liu237744.40
Jing Chen328560.83
zhang li430.39
Xiaoyang Liu527034.49