From World Afropedia
Jump to: navigation, search

Deep Web (also called the Deepnet,[1] Invisible Web,[2] or Hidden Web[3]) is the portion of World Wide Web content that is not indexed by standard search engines.

Mike Bergman, founder of BrightPlanet and credited with coining the phrase,[4] said that searching on the Internet today can be compared to dragging a net across the surface of the ocean: a great deal may be caught in the net, but there is a wealth of information that is deep and therefore missed.[5] Most of the Web's information is buried far down on sites, and standard search engines do not find it. Traditional search engines cannot see or retrieve content in the deep Web. The portion of the Web that is indexed by standard search engines is known as the Surface Web. As of 2001,[needs update] the deep Web was several orders of magnitude larger than the surface Web.[6]

It should be noted that the Deep Web is a separate entity from the dark Internet, which is made up of computers that can no longer be reached via the Internet. Also, the Dark Web - which consists of various anonymizing networks like Tor and the resources that they provide access to - is not synonymous with the Deep Web, but is considered as a subsection of it.[7][8]

Although much of the Deep Web is innocuous, some prosecutors and government agencies, among others, are concerned that the Deep Web is a haven for serious criminality.[9]


Bright Planet, a web-services company, describes the size of the Deep Web in this way:

It is impossible to measure, and hard to put estimates on, the size of the deep web because the majority of the information is hidden or locked inside databases. Early estimates suggested that the deep web is 400 to 550 times larger than the surface web. However, since more information and sites are always being added, it can be assumed that the deep web is growing exponentially at a rate that cannot be quantified.

Estimates based on extrapolations from a study done at University of California, Berkeley in 2001[6] speculate that the deep web consists of about 7.5 petabytes. More accurate estimates are available for the number of resources in the deep Web: research of He et al. detected around 300,000 deep web sites in the entire Web in 2004,[10] and, according to Shestakov, around 14,000 deep web sites existed in the Russian part of the Web in 2006.[11]


Bergman, in a seminal paper on the deep Web published in The Journal of Electronic Publishing, mentioned that Jill Ellsworth used the term invisible Web in 1994 to refer to websites that were not registered with any search engine.[6] Bergman cited a January 1996 article by Frank Garcia:[12]

It would be a site that's possibly reasonably designed, but they didn't bother to register it with any of the search engines. So, no one can find them! You're hidden. I call that the invisible Web.

Another early use of the term Invisible Web was by Bruce Mount and Matthew B. Koll of Personal Library Software, in a description of the @1 deep Web tool found in a December 1996 press release.[13]

The first use of the specific term Deep Web, now generally accepted, occurred in the aforementioned 2001 Bergman study.[6]


Methods which prevent web pages from being indexed by traditional search engines may be categorized as one or more of the following:

  • Dynamic content: dynamic pages which are returned in response to a submitted query or accessed only through a form, especially if open-domain input elements (such as text fields) are used; such fields are hard to navigate without domain knowledge.
  • Unlinked content: pages which are not linked to by other pages, which may prevent Web crawling programs from accessing the content. This content is referred to as pages without backlinks (also known as inlinks). Also, search engines do not always detect all backlinks from searched web pages.
  • Private Web: sites that require registration and login (password-protected resources).
  • Contextual Web: pages with content varying for different access contexts (e.g., ranges of client IP addresses or previous navigation sequence).
  • Limited access content: sites that limit access to their pages in a technical way (e.g., using the Robots Exclusion Standard or CAPTCHAs, or no-store directive which prohibit search engines from browsing them and creating cached copies.[14])
  • Scripted content: pages that are only accessible through links produced by JavaScript as well as content dynamically downloaded from Web servers via Flash or Ajax solutions.
  • Non-HTML/text content: textual content encoded in multimedia (image or video) files or specific file formats not handled by search engines.
  • Software: Certain content is intentionally hidden from the regular internet, accessible only with special software, such as Tor. Tor allows users to access websites using the .onion host suffix anonymously, hiding their IP address. Other such software includes I2P and darknet software.

Indexing the Deep Web

While it is not always possible to directly discover a specific web server's content so that it may be indexed, a site potentially can be accessed indirectly (due to computer vulnerabilities).

To discover content on the Web, search engines use web crawlers that follow hyperlinks through known protocol virtual port numbers. This technique is ideal for discovering content on the surface Web but is often ineffective at finding Deep Web content. For example, these crawlers do not attempt to find dynamic pages that are the result of database queries due to the indeterminate number of queries that are possible.[4] It has been noted that this can be (partially) overcome by providing links to query results, but this could unintentionally inflate the popularity for a member of the deep Web.

DeepPeep, Intute, Deep Web Technologies, Scirus, and are a few search engines that have accessed the Deep Web. Intute ran out of funding and is now a temporary static archive as of July, 2011.[15] Scirus retired near the end of January, 2013.[16]

Researchers have been exploring how the Deep Web can be crawled in an automatic fashion, including content that can be accessed only by special software such as Tor. In 2001, Sriram Raghavan and Hector Garcia-Molina (Stanford Computer Science Department, Stanford University)[17][18] presented an architectural model for a hidden-Web crawler that used key terms provided by users or collected from the query interfaces to query a Web form and crawl the Deep Web content. Alexandros Ntoulas, Petros Zerfos, and Junghoo Cho of UCLA created a hidden-Web crawler that automatically generated meaningful queries to issue against search forms.[19] Several form query languages (e.g., DEQUEL[20]) have been proposed that, besides issuing a query, also allow extraction of structured data from result pages. Another effort is DeepPeep, a project of the University of Utah sponsored by the National Science Foundation, which gathered hidden-Web sources (Web forms) in different domains based on novel focused crawler techniques.[21][22]

Commercial search engines have begun exploring alternative methods to crawl the deep Web. The Sitemap Protocol (first developed, and introduced by Google in 2005) and mod oai are mechanisms that allow search engines and other interested parties to discover deep Web resources on particular Web servers. Both mechanisms allow Web servers to advertise the URLs that are accessible on them, thereby allowing automatic discovery of resources that are not directly linked to the surface Web. Google's deep Web surfacing system pre-computes submissions for each HTML form and adds the resulting HTML pages into the Google search engine index. The surfaced results account for a thousand queries per second to deep Web content.[23] In this system, the pre-computation of submissions is done using three algorithms:

  1. selecting input values for text search inputs that accept keywords,
  2. identifying inputs which accept only values of a specific type (e.g., date), and
  3. selecting a small number of input combinations that generate URLs suitable for inclusion into the Web search index.

In 2008, to facilitate users of Tor hidden services in their access and search of a hidden .onion suffix, Aaron Swartz designed Tor2web—a proxy application able to provide access by means of common web browsers.[24] Using this application, Deep Web links appear as a random string of letters followed by the .onion TLD. For example, http://xmh57jrzrnw6insl followed by .onion, links to TORCH, the Tor search engine web page.

Classifying resources

Most of the work of classifying search results has been in categorizing the surface Web by topic. For classification of deep Web resources, Ipeirotis et al.[25] presented an algorithm that classifies a deep Web site into the category that generates the largest number of hits for some carefully selected, topically-focused queries. Deep Web directories under development include OAIster at the University of Michigan, Intute at the University of Manchester, Infomine[26] at the University of California, Riverside, and DirectSearch (by Gary Price). This classification poses a challenge while searching the deep Web whereby two levels of categorization are required. The first level is to categorize sites into vertical topics (e.g., health, travel, automobiles) and sub-topics according to the nature of the content underlying their databases.

The more difficult challenge is to categorize and map the information extracted from multiple deep Web sources according to end-user needs. Deep Web search reports cannot display URLs like traditional search reports. End users expect their search tools to not only find what they are looking for, but to be intuitive and user-friendly. In order to be meaningful, the search reports have to offer some depth to the nature of content that underlie the sources or else the end-user will be lost in the sea of URLs that do not indicate what content lies beneath them. The format in which search results are to be presented varies widely by the particular topic of the search and the type of content being exposed. The challenge is to find and map similar data elements from multiple disparate sources so that search results may be exposed in a unified format on the search report irrespective of their source.

See also


  1. Hamilton, Nigel. The Mechanics of a Deep Net Metasearch Engine. CiteSeerX: 
  2. Devine, Jane; Egger-Sider, Francine (July 2004). "Beyond google: the invisible web in the academic library". The Journal of Academic Librarianship. 30 (4): 265–269. Retrieved 2014-02-06.
  3. Raghavan, Sriram; Garcia-Molina, Hector (11–14 September 2001). "Crawling the Hidden Web". 27th International Conference on Very Large Data Bases. Rome, Italy.
  4. 4.0 4.1 Wright, Alex (2009-02-22). "Exploring a 'Deep Web' That Google Can't Grasp". The New York Times. Retrieved 2009-02-23.
  5. Bergman, Michael K (July 2000). The Deep Web: Surfacing Hidden Value (PDF). BrightPlanet LLC.
  6. 6.0 6.1 6.2 6.3 Bergman, Michael K (August 2001). "The Deep Web: Surfacing Hidden Value". The Journal of Electronic Publishing. 7 (1). doi:10.3998/3336451.0007.104.
  7. What is the Dark Web? How to access the Dark Web - How to turn out the lights and access the Dark Web (and why you might want to)
  8. Clearing Up Confusion – Deep Web vs. Dark Web
  9. The Secret Web: Where Drugs, Porn and Murder Live Online
  10. He, Bin; Patel, Mitesh; Zhang, Zhen; Chang, Kevin Chen-Chuan (May 2007). "Accessing the Deep Web: A Survey". Communications of the ACM (CACM). 50 (2): 94–101. doi:10.1145/1230819.1241670.
  11. Shestakov, Denis (2011) (PDF). Sampling the National Deep Web. pp. 331–340. 
  12. Garcia, Frank (January 1996). "Business and Marketing on the Internet". Masthead. 15 (1). Archived from the original on 1996-12-05. Retrieved 2009-02-24.
  13. @1 started with 5.7 terabytes of content, estimated to be 30 times the size of the nascent World Wide Web; PLS was acquired by AOL in 1998 and @1 was abandoned. "PLS introduces AT1, the first 'second generation' Internet search service" (Press release). Personal Library Software. December 1996. Retrieved 2009-02-24.
  14. "Hypertext Transfer Protocol (HTTP/1.1): Caching". Internet Engineering Task Force. 2014. Retrieved 2014-07-30.
  15. "Intute FAQ". Retrieved October 13, 2012.
  17. Sriram Raghavan; Garcia-Molina, Hector (2000) (PDF). Crawling the Hidden Web. Stanford Digital Libraries Technical Report. Retrieved 2008-12-27. 
  18. Raghavan, Sriram; Garcia-Molina, Hector (2001). "Crawling the Hidden Web" (PDF). Proceedings of the 27th International Conference on Very Large Data Bases (VLDB). pp. 129–38.
  19. Alexandros, Ntoulas; Zerfos, Petros; Cho, Junghoo (2005) (PDF). Downloading Hidden Web Content. UCLA Computer Science. Retrieved 2009-02-24. 
  20. Shestakov, Denis; Bhowmick, Sourav S.; Lim, Ee-Peng (2005). "DEQUE: Querying the Deep Web" (PDF). Data & Knowledge Engineering 52(3): 273–311. 
  21. Barbosa, Luciano; Freire, Juliana (2007) (PDF). An Adaptive Crawler for Locating Hidden-Web Entry Points. WWW Conference 2007. Retrieved 2009-03-20. 
  22. Barbosa, Luciano; Freire, Juliana (2005). Searching for Hidden-Web Databases.. WebDB 2005. Retrieved 2009-03-20. 
  23. Madhavan, Jayant; Ko, David; Kot, Łucja; Ganapathy, Vignesh; Rasmussen, Alex; Halevy, Alon (2008) (PDF). Google’s Deep-Web Crawl. VLDB Endowment, ACM. Retrieved 2009-04-17. 
  24. Aaron, Swartz. "In Defense of Anonymity". Retrieved 4 February 2014.
  25. Ipeirotis, Panagiotis G.; Gravano, Luis; Sahami, Mehran (2001). "Probe, Count, and Classify: Categorizing Hidden-Web Databases" (PDF). Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data. pp. 67–78.

Further reading