HIGWGET-A Model for Crawling Secure Hidden WebPages

International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.3, No.2, March 2013

HIGWGET-A Model for Crawling Secure Hidden
WebPages
K.F. Bharati1, Prof. P. Premchand2 and Prof. A Govardhan3
1
Asst.Professor, CSE Dept, JNTUACEA, Anantapur
kfbharathi@gmail.com
2
Dean, Faculty of Engineering, CSE Dept, UCEOU, Osmania University,
Hyderabad
p.premchand@uceou.edu
3
Director of Evaluation, JNTUH, Hyderabad
govardhan_cse@jntuh.ac.in

ABSTRACT
The conventional search engines existing over the internet are active in searching the appropriate
information. The search engine gets few constraints similar to attainment the information seeked from a
different sources. The web crawlers are intended towards a exact lane of the web.Web Crawlers are limited
in moving towards a different path as they are protected or at times limited because of the apprehension of
threats. It is possible to make a web crawler,which will have the ability of penetrating from side to side the
paths of the web, not reachable by the usual web crawlers, so as to get a improved answer in terms of
infoemation, time and relevancy for the given search query. The proposed web crawler is designed to
attend Hyper Text Transfer Protocol Secure (HTTPS) websites including the web pages,which requires
verification to view and index.

KEYWORDS
Deep Web Crawler, Hidden Pages, Accessing Secured Databases, Indexing.

1. INTRODUCTION
The functioning of a deep web crawler differs with the functioning of a conventional web crawler
in quite a few aspects, at first the web, taken as a graph by the web crawler that has to be
traversed in a different path to go into a secure and controlled network. Mostly the web crawlers
are separated into a number of categories listed below.

a) Dynamic Web Crawler: The crawler proceeds active content in answer to the
submitted. The main search attribute for this kind of web crawler is text fields.
b) Unlinked Pages/Content: more than a few pages are not associated to any other
in/back links preventing them to be set up by search engines. These contents are
referred to as back links.
c) Private Pages/Web: A number of sites that are administered by organisation and
contain certain copyrighted material needs a registration to access it. There is also a
possibility of the website to ask the user to authenticate. Most of these pages are
encrypted and may also require Digital Signature for the browser to access.
DOI : 10.5121/ijdkp.2013.3202 23


d) Context Oriented Web: These web pages are accessible only by a range of IP
addresses are kept in the intranet, that are ready to be accessed by internet too.
e) Partial Access Web: several pages limit the access of their pages to avoid search
engine to display the content in a technical way, by the use of Captcha code and
restriction of meta data, preventing the web crawler’s entry.
f) Scripted Web Content: pages are accessible only through the link provided by web
servers or name space provided by the cloud. Some video, flash content and applets
will also falls under this category.
g) Non-HTML Content: Certain content embedded in image and video files are not
handled by search engines.

Usually searching internet works with the Hyper Text Transfer Protocol, but there exists other
protocols similar to HTTP as like File Transfer Protocol, Gopher,Hyper Text Transfer Protocol
Secure.These are also limit the conventional search engines searches the information.The paper
works with the techniques crawing the information to overcome the drawback of the traditional
web crawlers to crawl HTTPS sites. The whole web is divided into types of as shown in below
figure 1, the traditional web and the hidden web [25, 26, 27].

Figure 1: classification of Web

The traditional web normally deploys by common use search engine. The hidden web cannot be
traversed straightly by a common use search engine. Internet survey says that there are about
3,00,000 Hidden Web databases [28]. Few qualities of the hidden web contains are containing
high quality contents exceeding all print data available.

2. RELATED WORK
There exists a number of web crawlers, which are intended to search hidden web pages. A
periodical study of such web crawler is being done here so as to identify their restrictions to
overcome the same in the proposed structure. In the way of Setting the unimportant blocks from
the web pages can make possible search and to get better the web crawler has been proved. This
way can make possible even to search hidden web pages [3]. Similar to this there is are the most
popular DOM-based segmentation [5], Location-Based Segmentation [10] and Vision-Based
Page Segmentation [4]. The paper deals with ability of differentiating features of the web page as
blocks. Modelling is done on the same to find some insights to get the information of the page by
using two methods based on neural network and Support Vector Machine (SVM) facilitating the
page to be found.
The availability of robust, flexible Information Extraction (IE) systems for transforming the Web
pages into algorithm. Program readable structures like one as relational database that will help
24


the search engine to search easily[6]. The problem of extracting website skeleton, i.e. extracting
the underlying hyperlink structure used to organize the content pages in a taken website. They
have proposed an automated Back On Topic (BOT) like algorithm that has the functionality of
discovering the skeleton of a given website. The (SEW) Search Engine Watch algorithm, it
examines hyperlinks in groups and identifies the navigation links that point to pages in the next
level in the website structure. Here the entire skeleton is then constructed by recursively fetching
pages pointed by the discovered links and analysing these pages using the same process is
explained [7].

2.1 Substitute Techniques for Web Crawlers
The subject of taking out of search term for over millions and billions of information have
touched upon the issue of scalability and how approaches can be made for a very large databases
[8]. These papers have focused completely on current day crawlers and their inefficiencies in
capturing the correct data. This analysis covers the concept of Current-day crawlers retrieving
content only from the Publicly Index able Web (PIW), the pages reachable only by following
hypertext links and ignoring the pages that require certain authorization or prior registration for
viewing them [9]. The different characteristics of web data, the basic mechanism of web mining
and its several types are summarized. The reason for the usage of web mining for the crawler
functionality is well explained here in the paper. Even the limitations of some of the algorithms
are listed. The paper talks about the usage of fields like soft computing, fuzzy logic, artificial
networks and genetic algorithms for the creation of crawler. The paper gives the reader the future
design that can be done with the help of the alternate technologies available [11].

2.2 Intelligent Web Agents
The soon part of the paper deals with telling the characteristics of web data, the different
components, types of web mining and the boundaries of existing web mining methods. The
applications that can be done with the help of these substitute techniques are also described. The
study involved in the paper is in-depth and surveys all systems which aim to dynamically extract
information from unfamiliar resources. Intelligent web agents are available to search for related
information using characteristics of a particular domain got from the user profile to organize and
interpret the discovered information. There are several available agents such as Harvest [15],
FAQ-Finder [15], Information Manifold [16], OCCAM [[17], and Parasite [18], that rely on the
predefined domain specific template information and are experts in finding and retrieving exact
information. The Harvest [15] system depends upon the semi-structured documents to extract
information and it has the capability to exercise a search in a latex file and a post-script file. At
most used well in bibliography search and reference search, is a great tool for researchers as it
searches with key terms like authors and conference information. In the same way FAQ-Finder
[15], is a great tool to answer Frequently Asked Questions (FAQs), by collecting answers from
the web. The other systems described are ShopBot [20] and Internet Learning Agent [21]
retrieves product information from numerous vendor website using generic information of the
product domain. The Features of different web crawlers are as shown in table1.
2.3 Ranking
The developing web architecture and the behaviour of web search engines have to be altered in
order to get the desired results [12]. In [13] the authors’ talk about ranking based search tools like
Pub med that allows users to submit highly expressive Boolean keyword queries, but ranks the
query results by date only. A proposed approach is to submit a disjunctive query with all query
keywords, retrieve all the returned matching documents, and then rerank them. The user fills up a
25


form in order to get a set of relevant data. The process is tedious for a long run and when the
number of data to be retrieved is huge, is discussed [14]. In the thesis by Tina Eliassi-Rad, a
number of works that retrieve hidden pages are discussed.

Table 1. Features of Web Crawlers

SNO TYPES OF WEB FEATURES
CRAWLERS
1 Gnu Wget • Can resume aborted downloads.
• Can use filename wild cards and recursively
mirror directories.
• Supports HTTP proxies and HTTP cookies.
• Supports persistent HTTP connections.
2 WebSphinix • Multithreaded Web page retrieval
• An object model that explicitly represents
pages and links
• Supports for reusable page content classifiers
• Support for the robot exclusion standard
3 Heritrix • Ability to run multiple crawl jobs
simultaneously.
• Ability to browse and modify the configured
Spring beans.
• Increased scalability.
• Increased flexibility when modifying a
running crawl
4 J-Spider • Checks sites for errors.
• Outgoing and/or internal link checking.
• Analyze site structure.
• Download complete web sites

There are many proposed hidden pages techniques, which are an unique web crawler algorithm to
do the hidden page search [23]. An architectural model for extracting hidden web data is
presented [24]. The end of the survey circumstances that much less work has been carried out an
advanced form based search algorithm that is even capable of filling forms and captcha codes.

3. THE PROPOSED APPROACH
Consider a situation, where a user is to look for a term “ipad”.The major focus of a traditional
crawler will record a set of search results mostly consisting of the information about the search
term and certain shopping options for the search term “ipad”. But, it might leave out several
websites with best offer on the same search term “ipad” as it involves, only a registered user to
give verification credentials to sight the product pricing and review details. The basic need of the
search engine is to go into such type of web pages, after filling the username and password.
Enabling the web crawler to do the same is the primary importance given in the paper.
An already accessible PIW crawler is in use and the automatic form filling concept is attached
and the results are analysed using several different search terms. The proposed algorithm will be
analysing most of the Websites and will tend to pull out the related pages of the search query. The
26


URL’s of the pages are recognized and are added to the URL repository. The role of parser comes
to live at this moment and it sees for any extended URL’s from the primary source of URL. The
analyser will be co-working with the parser and will extract finite information from the web page.
It scans every page for the search terms by analysing each and every sentence by breaking them
and retrieves the essential information before showing the page. The composer will then compose
the details of the web pages in a database. This is how a typical hidden-pages web crawler works.
The analyser sees for the web page with more number of terms relevant to the search query. It has
a counter, which will be initialised and the counter increments as soon as some of the words in
the web page are found similar to that of the search term. The web page of web site with more
counter value are analysed and numbered and they are projected in page-wise as search results.

3.1 Proposed Work

The traditional mode of working of the hidden web crawler is taken into account as a skeleton
and several improvements are done after finding out its limitations and constraints from the
literature survey. The crawler has to be given capabilities to find out hidden pages better than the
existing hidden crawlers [9]. For the same, certain extra module has to be added with the existing
modules of hidden crawler. The added module is named as structure module capable of filling
authentication forms before entering the web site, if needed. The module facilitates the crawler to
enter a Secure Hyper Text Mark-up Page. Almost all the e-shopping sites has https as their
transport protocol and this ability will lead to get information form, for this kind of web sites,
which are not visible to ordinary web crawlers. The web crawler writes down the websites found
in a particular domain in text files, enabling easy access. The list divides the good and bad pages,
according to certain attributes of the webpage. The proposed web crawler will also be legible to
crawl through Ajax and java script oriented pages.

3.2 Design Modules

The architecture of prototype Web Crawler is shown in Figure 2

Figure 2. The Web Crawler Architecture

3.2.1 Analyser
The primary component of the web crawler is the analyser, which capable of looking in to the
web pages. The module is after the structure module, which is a search form used by the user to
give search term and also his credentials. The analyser will scan each and every page and will
keep the vital information in a text file. The files got as an outcome of the analyser phase is a text
27


file consisting of all the website information and is stored in a log database, for further use for
another search query.
3.2.2 Parser and Composer
The primary function of the parser in the proposed approach is to take the document and splitting
it into index- able text segments, letting it to work with different file formats and natural
languages. Mostly linguistic algorithms are applied as parser. Here a traditional parser algorithm
is used. The composer will compose the data of the web pages in the database.
3.2.3 Indexer
The function of indexer is dependent on parser and builds the indexes necessary to complement
the search engine. This part decides the power of the search engine and determines the results for
each of the search word. The proposed indexer has the capability to index terms and words from
secure as well as open web. The difference between the normal web crawler and hidden page web
crawler is shown here. The Google’s web indexer is supposed to be the best and uses ranking
algorithm and changes the terms of the web pages as per their popularity and updating, making it
a dynamic indexer. The proposed web indexer has the capability to fill search words within the
web pages and find out results, as well as concentrating on secure pages with HTTPS too.
3.2.4 Result Analyser
The result analyser explores the searched results and gives the same in a GUI based structure for
the developer to identify and come out with modifications. It is done by inputting a web page and
all the HTML tags of it are considered to be output.

4. IMPLEMENTATION
As part of implementation an open source web crawler was identified. There are several open
source web crawlers available and some of them are Heritrix [28], an internet Archive's open-
source, extensible, web-scale, archival-quality web crawler that is web-scalable and extensible.
WebSPHINX [29] is a Website-Specific Processors for HTML Information extraction and is
based on java and gives an interactive development environment for creating web crawlers.
JSpider [30], is a highly configurable and customizable Web Spider engine written purely in java.
Web-Harvest [31] is an Open Source Web Data Extraction tool written in Java and focuses
mainly on HTML/XML based web sites. JoBo [32] is a simple program to download complete
websites to your local computer.
For the implementation of our specific method which can make use of a different pattern of
search to mine the searches via HTTPs, HTTP and FTP and also has the capability of getting
information from preregistration–then only access sites, GNU Wget is downloaded and modified.
GNU Wget is a freely distributed, GNU licensed software package for retrieving files via HTTP,
HTTPS and FTP. It is a command based tool. tool when examined showed visible improvement
and some resultant pages from HTTPs and a form filled web site. Figures 3,4,5 shows the
comparison of crawlers in terms of releted pages,Depth and Time.

28


Figure 3. The Comparison of Different Web Crawlers in terms of related words

Figure 4. The Comparison of Different Web Crawlers in terms of Depth

Figure 5. The Comparison of Different Web Crawlers in terms of Time

5. OBSERVATIONS AND RESULTS
The results are taken for several keywords to find out the proposed Hidden web page web
crawler’s difference from the conventional web search engine and a better search is found, which
includes several secure and hidden pages input in the search results. The results proved that the
model the HiGwget shows better results.

29


6. CONCLUSION
With the advent of search is increasing exponentially people and corporate rely on searches for
multiple decision making, search engine with newer and wider results including pages that are
rare and useful. The proposed Hidden page web crawler, makes use of integration of several
secure web pages as a part of indexing and comes out with a better result. In future the same can
be applied for a mobile search which can be extended for ecommerce application and also
crawling time can be reduced.

REFERENCES

[1] S. Lawrence, C.L. Giles, "Accessibility of Information On the Web," Nature,400,107-109, (1999).
[2] Djoerd Hiemstra: Using Language Models for Information Retrieval. Univ. Twente 2001: I-VIII, 1-
163..
[3] Ruihua Song, Haifeng Liu, Ji-Rong Wen, Wei-Ying Ma: Learning Important Models for Web Page
Blocks Based On Layout and Content Analysis.SIGKDD Explorations 6(2): 14-23 (2004).
[4] Cai, D.Yu,S.Wen,J.-R.and Ma, W.-Y.,VIPS:AVision Based Page Segmentation Algorithm,Microsoft
Technical Report, MSR-TR-2003-79,( 2003).
[5] Chen, J., Zhou B.,Shi,J., Zhang, H.-J. and Qiu,F., Function-Based Object Model Toward Website
Adaptation,in the proceedings of the 0th World Wide Web conference (WWW10), Budapest,
Hungary, May (2001).
[6] XML Chia-Hui Chang, Mohammed Kayed, Moheb R.Girgis,Khaled F.Shaalan: A Survey of Web
Information Extraction Systems. IEEE Trans. Knowl. Data Eng. 18(10): 1411-1428 (2006)
[7] Zehua Liu,Wee Keong Ng,Ee-Peng Lim: An Automated Algorithm for Extracting Website Skeleton.
DASFAA 2004: 799-811.
[8] Eugene Agichtein: Scaling Information Extraction to Large Document Collections.IEEE Data Eng.
Bull.28(4): 3-10 (2005).
[9] Sriram Raghavan, Hector Garcia-Molina: Crawling the Hidden Web. VLDB 2001: 129-138.
[10] Kovacevic, M.,Diligenti, M., Gori, M.and Milutinovic, V.,Recognition of Common Areas in a Web
Page Using Visual Information: A Possible Application in a Page Classification,in the proceedings of
2002 IEEE International Conference on Data Mining (ICDM'02), Maebashi City, Japan, December,
(2002).
[11] Sankar K. Pal, Varun Talwar, Pabitra Mitra: Web Mining in Soft Computing Framework:
relevance,state of the art and future directions.IEEE Transactions on Neural Networks 13(5): 1163-
1177 (2002).
[12] Fabrizio Lamberti, Andrea Sanna, Claudio Demartini: A Relation-Based Page Rank Algorithm for
Semantic Web Search Engines.IEEE Trans. Knowl. Data Eng.21(1): 123-136 (2009)
[13] Vagelis Hristidis, Yuheng Hu, Panagiotis G. Ipeirotis:Relevance-Based Retrieval on Hidden-Web
Text Databases Without Ranking Support. IEEE Trans. Knowl. Data Eng. 23(10): 1555-1568 (2011)
[14] Stephen W. Liddle,Sai Ho Yau, David W. Embley:On the Automatic Extraction of Data from the
Hidden Web. ER (Workshops) 2001: 212-226
[15] K. Hammond, R. Burke, C. Martin, and S. Lytinen,“Faq-finder: A Case Based Approach to
Knowledge Navigation,” presented at the Working Notes of AAAI Spring Symposium on
Information Gathering From Heterogeneous Distributed Environments, Stanford, CA, (1995).
[16] A.Y.Levy,T. Kirk,and Y.Sagiv, “The Information Manifold,” presented at the AAAI Spring
Symposium on Information Gathering From Heterogeneous Distributed Environments, (1995).
[17] C.Kwok and D.Weld, “Planning to Gather Information,” in Proc.14th Nat. Conf. AI,(1996).
[18] E. Spertus, “Parasite: Mining Structural Information on the Web,” presented at the Proc. 6th WWW
Conf., (1997).
[19] O. Etzioni, D.S.Weld,and R.B.oorenbos, “A Scalable Comparison Shopping Agent for the World
Wide Web,” Univ. Washington, Dept.Comput. Sci.,Seattle, Tech. Rep. TR 96-01-03, (1996).

30

[20] O. Etzioni and M.Perkowitz, “Category translation: Learning to Understand Information on the
Internet,” in Proc. 15th Int. Joint Conf. Artificial Intell, Montreal, QC, Canada, 1995, pp. 930–936.
[21] M.Craven, D. Freitag,A.McCallum,T. Mitchell, K. Nigam, S. Slattery, and DiPasquo, “Learning to
Extract Symbolic Knowledge from the world wide Web,” in Proc. 15th Nat. Conf. AI (AAAI98),
1998, pp.509–516.
[22] Anuradha, A.K.Sharma, “A Novel Approach for Automatic Detection and Unification of Web Search
Query Interfaces Using Domain Ontology” selected in International Journal of Information
Technology and Knowledge Management (IJITKM), August (2009).
[23] S.Raghavan and H. Garcia-Molina. Crawling the Hidden Web. In Proceedings of VLDB, pages
129–138, (2001).
[24] Arvind Arasu, Junghoo Cho, Hector Garcia-Molina, Andreas Paepcke, and Sriram Raghavan,
“Searching the Web”, ACM Transactions on Internet Technology (TOIT), 1(1):2–43, August (2001).
[25] Mike Burner, “Crawling towards Eternity: Building an archive of the World Wide Web”,
WebTechniques Magazine, 2(5), May (1997).
[26] Brian E. Brewington and George Cybenko. “How Dynamic is the Web.” In Proceedings of the Ninth
International World-Wide Web Conference, Amsterdam, Netherlands, May (2000).
[27] Michael K. Bergman, “The Deep Web: Surfacing Hidden Value”, Journal of Electronic
Publishing,7(1), 2001.
[28] Crawler.archive.org/index.html
[29] http://www.cs.cmu.edu/~rcm/websphinx/
[30] http://j-spider.sourceforge.net/
[31] web-harvest.sourceforge.net/
[32] www.matuschek.net/jobo/

Authors
K. F. Bharati, Asst. Prof., Dept. Of CSE, JNTUACEA, Anantapur
B. Tech From University of Gulbarga
M. Tech from Visveswariah Technological University, Belgaum
Officer Incharge for Central Computer Center,JNTUACEA

P. Premchand Prof, Dean, Faculty of Engineering, CSE Dept. UCEOU,
Osmania University, Hyderabad.
B. Sc(Electrical Engineering), RIT., Jamshedpur
M.E(Computer Science), Andhra University
Ph.D(Computer Science & Systems Engineering), Andhra University, Visakhapatnam

Prof. A. Govardhan, Director of Evaluation, JNTUH, Hyderabad
He has done BE in Computer Science and Engineering from Osmania University
College of Engineering, Hyderabad in 1992.
M.Tech from Jawaharlal Nehru University(JNU), Delhi in 1994
and his Ph.D from Jawaharlal Nehru Technological University,
Hyderabad (JNTUH) in 2003. He was awarded with “NATIONAL
INTEGRATION AWARD by HEALTH CARE INTERNATIONAL” and “Dr
SARVEPALLY RADHAKRISHNA by A.P STATE CULTURAL AWARENESS SOCIETY” in 2013.In
2012 He was awarded as “The Best Teacher “.
He was awarded Telugu Vignana Parithoshikam, by the Government of Andhra Pradesh for B.E(CSE)
program.
He has been a committee member for various International and National conferences including
PAKDD2010, IKE10 ,ICETCSE-2010 ICACT-2008, NCAI06.

31

HIGWGET-A Model for Crawling Secure Hidden WebPages

More Related Content

What's hot

Viewers also liked

Similar to HIGWGET-A Model for Crawling Secure Hidden WebPages

More from ijdkp

Recently uploaded

HIGWGET-A Model for Crawling Secure Hidden WebPages