Trusted Risk Evaluation and Attribute Analysis in Ad-Hoc Networks Security Mechanism based on Projection Pursuit Principal Component Analysis

2010 ◽  
Author(s):  
Jihang Ye ◽  
Mengyao Liu ◽  
Cai Fu
Keyword(s):  
Principal Component Analysis ◽  
Ad Hoc Networks ◽  
Risk Evaluation ◽  
Ad Hoc ◽  
Projection Pursuit ◽  
Principal Component ◽  
Component Analysis ◽  
Security Mechanism ◽  
Attribute Analysis ◽  
Hoc Networks

Evaluation of Feature Reduction using Principal Component Analysis and Sequential Pattern Matching for Manet

2017 ◽  
Vol 7 (3) ◽  
pp. 1228
Author(s):  
M. Reji ◽  
P.C. Kishore Raja ◽  
Bhagyalakshmi M
Keyword(s):  
Principal Component Analysis ◽  
Ad Hoc ◽  
Pattern Mining ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Reduction ◽  
Sequential Pattern ◽  
Basic Part ◽  
Information Set ◽  
Hoc Networks

In Mobile Ad hoc Networks (MANETs) there are some security problems because of portability, element topology changes, and absence of any framework. In MANETs, it is of extraordinary significance to identify inconsistency and malignant conduct. With a specific end goal to recognize malignant assaults by means of interruption identification frameworks and dissect the information set, we have to choose some components. Thus, highlight determination assumes basic part in recognizing different assaults. In the writing, there are a few recommendations to choose such elements. For the most part, Principal Component Analysis (PCA) breaks down the information set and the chose highlights. In this paper, we have gathered a list of capabilities from some cutting edge works in the writing. Really, our reproduction demonstrates this list of capabilities identify inconsistency conduct more precise. Likewise, interestingly, we utilize PCA for investigating the information set. In contrast to PCA, our results show Sequential pattern mining (SPM) cannot be affected by outlier data within the network. The  normal and attack states are simulated and the results are analyzed using NS2 simulator.

scholarly journals Security mechanism for voice over multipath mobile ad hoc networks

2011 ◽  
Vol 11 (2) ◽  
pp. 196-210 ◽  
Author(s):  
Binod Vaidya ◽  
Mieso K. Denko ◽  
Joel J. P. C. Rodrigues
Keyword(s):  
Ad Hoc Networks ◽  
Mobile Ad Hoc Networks ◽  
Ad Hoc ◽  
Security Mechanism ◽  
Mobile Ad Hoc ◽  
Hoc Networks

Evaluation of Knowledge Resource Service Ability Based on Robust Projection Pursuit - Principal Component Analysis under Cloud Manufacturing Environment

2013 ◽  
Vol 397-400 ◽  
pp. 42-46
Author(s):  
Nan Zhao ◽  
Hong Yu Shao
Keyword(s):  
Principal Component Analysis ◽  
Projection Pursuit ◽  
Principal Component ◽  
Component Analysis ◽  
Cloud Manufacturing ◽  
Assessment Model ◽  
Design Knowledge ◽  
Manufacturing Environment ◽  
Knowledge Resource ◽  
Resource Service

According to the current situations of the unorganized and disorderly design knowledge as well as the weak innovation capability for SMEs under cloud manufacturing environment, and aiming at combining the design knowledge into ordered knowledge resource series, the service ability assessment model of knowledge resource was eventually proposed, and moreover, the Projection Pursuit-Principal Component Analysis (PP-PCA) algorithm for service ability assessment was further designed. The study in this paper would contribute to the realization of the effectiveness and accuracy of the knowledge push service, which exhibited a significant importance for improving the reuse efficiency of knowledge resources and knowledge service satisfaction under the cloud manufacturing environment.

scholarly journals Projection in genomic analysis: A theoretical basis to rationalize tensor decomposition and principal component analysis as feature selection tools

2020 ◽  
Author(s):  
Y-h. Taguchi ◽  
Turki Turki
Keyword(s):  
Principal Component Analysis ◽  
Gene Selection ◽  
Statistical Tests ◽  
Projection Pursuit ◽  
Null Distribution ◽  
Genomic Analysis ◽  
Principal Component ◽  
Tensor Decomposition ◽  
Component Analysis ◽  
Singular Value

ABSTRACTIdentifying differentially expressed genes is difficult because of the small number of available samples compared with the large number of genes. Conventional gene selection methods employing statistical tests have the critical problem of heavy dependence of P-values on sample size. Although the recently proposed principal component analysis (PCA) and tensor decomposition (TD)-based unsupervised feature extraction (FE) has often outperformed these statistical test-based methods, the reason why they worked so well is unclear. In this study, we aim to understand this reason in the context of projection pursuit that was proposed a long time ago to solve the problem of dimensions; we can relate the space spanned by singular value vectors with that spanned by the optimal cluster centroids obtained from K-means. Thus, the success of PCA- and TD-based unsupervised FE can be understood by this equivalence. In addition to this, empirical threshold adjusted P-values of 0.01 assuming the null hypothesis that singular value vectors attributed to genes obey the Gaussian distribution empirically corresponds to threshold-adjusted P-values of 0.1 when the null distribution is generated by gene order shuffling. These findings thus rationalize the success of PCA- and TD-based unsupervised FE for the first time.

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