18-04-2013, 04:43 PM
Web Mining Framework for Security in E-commerce
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Abstract
This paper is based on e-commerce web sites how to
use web mining technology for providing security on e-commerce
web sites. The connection between web mining ,security and ecommerce
analyzed based on user behavior on web .Different
web mining algorithms and security algorithm are used to
provided security on e-commerce web sites. Based on customer
behavior different web mining algorithms like page rank
algorithm and trust rank algorithm is used for developing web
mining framework in e-commerce web sites. We have developed
false hit database algorithm and nearest neighbor algorithm to
provide security on e-commerce web site. In existing web mining
framework is based on only web content mining, We have
proposed Web mining framework system which is based on web
structure mining analysis, Web Content Mining analysis,
decision analysis and security analysis.
WEB MINING FRAMEWORK SYSTEM
Web mining is the use of data mining techniques to
automatically discover and extract knowledge from web
documents.web mining is the information service centre for
news, e-commerce, and advertisement, government, education,
financial management, education, etc. We have developed Web
mining framework for evaluating ecommerce web sites .In
general web mining task can be classified into web content
mining, web structure mining and web usage mining. Some of
the well-known classification techniques for web mining such
as like, page rank algorithm and trust rank algorithm is used in
this paper. Our proposed web mining framework consists of
four phase’s web structure mining analysis, Web Content
Mining analysis, decision analysis and security analysis.
WEB STRUCTURE MINING ANALYSIS
This phase analyses a web site by using both page rank
algorithm and trust rank algorithm. The ranking of a page is
determined by its link structure instead of its content. The trust
rank algorithm is procedure to rate the quality of web sites. The
output is quality based score which correspond to trust
assessment level of the web site. The initial step is collects
information from web sites and stores those web pages into
web repository.
WEB CONTENT MINING ANALYSIS
Web content mining is defined as searching of new
information from web data. Data is retrieved for desired topic
by user. In Web content mining analysis we have taken
example job categories and the associated skills needs
prevalent in the computing professions. We performed a cluster
analysis on the ads in two phases. Hierarchical agglomerative
clustering is the first step to identify unique skill set clusters.
The classification of ads is validated into clusters by
performing k-means cluster analysis. Module1: User
Identification, Module2: Job Definition, Module3: Data
Collection, Module4: Data Analysis.
SECURITY ANALYSIS
We perform complete security analysis in this phase. 89%
of web development companies has not follow industry
standard in developing and hosting the websites they make.
The customers who use the web sites do know the difference
between a secure website and insecure website. We have
developed trust path intermediaries building algorithm, false hit
database algorithm and nearest neighbor algorithm to provide
security on e-commerce web site. Multi-step processing is used
for nearest neighbor and similarity search in application
involving web data and/or costly distance computations. CAMNC-
to reduce the size of False Hit database. The query is
authenticated. A server maintains dataset database signed by
trusted authority False hit Database to reduce hang or lag in the
server. Provides accurate data as well as NN result-set. We
have developed following modules for providing security on ecommerce
web sites. Module 1: Authentication, Module 2:
Query processing, Module 3: Similarity search and Module 4:
False hit reduction
CONCLUSION
In this paper we have proposed web mining framework for
e-commerce web sites. In web mining framework we have
developed four phases’ web structure mining analysis, Web
Content Mining analysis, decision analysis and security
analysis. In web structure mining analysis we have used page
rank algorithm and trust rank algorithm. In Web Content
Mining analysis we have used Hierarchical agglomerative
clustering and k-means cluster analysis. In decision analysis we
have used trust calculation of web site and statistical techniques
to analyses the result of the evaluation. In security analysis we
developed trust path intermediaries building algorithm, false hit
database algorithm and nearest neighbor algorithm to provide
security on e-commerce web site