The deep web, invisible web, or hidden web are parts of the World Wide Web whose contents are not indexed by standard search engines for any reason. The content is hidden behind HTML forms. It is estimated that the deep web makes up 96 % of the whole internet. The opposite term to the deep web is the surface web. The deep web includes many very common uses such as web mail and online banking but also paid for services with a paywall such as video on demand, and many more. Computer scientist Michael K. Bergman is credited with coining the term deep web in 2001 as a search indexing term.
Since then, the use in the Silk Road's media reporting, many people and media outlets, have taken to using Deep Web synonymously with the dark web or darknet, a comparison many reject as inaccurate and consequently is an ongoing source of confusion. Wired reporters Kim Zetter and Andy Greenberg recommend the terms be used in distinct fashions. While the deep web is reference to any site that cannot be accessed through a traditional search engine, the dark web is a small portion of the deep web that has been intentionally hidden and is inaccessible through standard browsers and methods.
In the year 2001, Michael K. Bergman said how searching on the Internet 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. 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[update], the deep web was several orders of magnitude larger than the surface web. An analogy of an iceberg used by Denis Shestakov represents the division between surface web and deep web respectively:
It is impossible to measure, and harsh 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 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, and, according to Shestakov, around 14,000 deep web sites existed in the Russian part of the Web in 2006.
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. Bergman cited a January 1996 article by Frank Garcia:
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.
The first use of the specific term deep web, now generally accepted, occurred in the aforementioned 2001 Bergman study.
Methods which prevent web pages from being indexed by traditional search engines may be categorized as one or more of the following:
- Contextual Web: pages with content varying for different access contexts (e.g., ranges of client IP addresses or previous navigation sequence).
- 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.
- 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).
- Non-HTML/text content: textual content encoded in multimedia (image or video) files or specific file formats not handled by search engines.
- Private Web: sites that require registration and login (password-protected resources).
- Software: certain content is intentionally hidden from the regular Internet, accessible only with special software, such as Tor, I2P, or other darknet software. For example, Tor allows users to access websites using the .onion server address anonymously, hiding their IP address.
- 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.
- Web archives: Web archival services such as the Wayback Machine enable users to see archived versions of web pages across time, including websites which have become inaccessible, and are not indexed by search engines such as Google.
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. 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 Ahmia.fi 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. Scirus retired near the end of January 2013.
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) 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. Several form query languages (e.g., DEQUEL) 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.
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 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. In this system, the pre-computation of submissions is done using three algorithms:
- selecting input values for text search inputs that accept keywords,
- identifying inputs which accept only values of a specific type (e.g., date), and
- 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. Using this application, deep web links appear as a random string of letters followed by the .onion TLD.
|Look up Deep Web in Wiktionary, the free dictionary.|
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