About Deep Web

Deep Web (also called the Deepnet,Invisible Web, or Hidden Web) 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, 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. 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, the deep Web was several orders of magnitude larger than the surface Web.
The deep web should not be confused with the dark Internet, computers that can no longer be reached via the Internet. The Darknet distributed file sharing network, can be classified as part of the Deep Web.
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.

Size

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

It is impossible to measure or put estimates onto 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.

Naming

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

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.)
• 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. 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 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. 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. 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. 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 at the University of California at 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.

how to access the deep web, deep web search engines, deep web tor, deep web como entrar, deep web search,

Deep Web Marketplaces

Over the past couple of weeks, I’ve been frequenting the deep web marketplaces most famously used for buying drugs online with Bitcoin.
I wanted to see if there was anything we could learn about how these illicit marketplaces work that could be applied to improve the legal marketplaces we invest in at USV.
As part of my research, I purchased an item on Evolution (no, not drugs – a pair of furry boots) in an effort to understand the dynamics of these marketplaces, from trust and safety to flow of funds. This is what I learned in the process.
Privacy

• Deep web marketplaces can only be accessed using Tor, a decentralized computer network that anonymizes traffic such that it’s harder to trace an individual user through their IP address. If you’d like to learn more about Tor and how it works, this is a good introduction.
  

• Most if not all marketplaces force you to sign up before browsing the listings. The sign up process involves picking a username and password, and an account PIN number. You’re also expected to remember a mnemonic private key for your account, which is not stored on the marketplace’s servers.
  

• Most sellers (particularly drug dealers) require all communications to be encrypted with PGP. Most marketplaces have a PGP key field at the profile setup level.
  

• Some marketplaces automatically delete all order information from their servers 30 days after an order has been “finalized” by the user.
  

• No e-mails are used, only Bitmessage (decentralized) or the marketplace’s messaging system (encrypted and periodically deleted).
  

Products

• Lots of drugs. The drugs category is 10x larger than all others. You’ll find anything from Valium to cocaine and LSD.
  

• You’ll also find digital content, stolen credit card and user/password lists, hacking services (mostly DDoS), counterfeit goods (fashion, jewelry, etc.), lab equipment, electronics (I was tempted to buy a pocket-sized EMP pulse generator), high-end spy gear, forged documents (driver’s licenses, passports), counterfeit currency, weapons and more.
  

Brand and Reputation

• Brand and reputation means everything to sellers. Buyers guide themselves via eBay-style reviews of the sellers.
  
• This is particularly important in an environment where there is no real identity shared between any of the participants. By contrast, I may not know who an eBay seller is but I take comfort in knowing that eBay does.
  

• Most sellers have 95%+ positive ratings. Some sellers have been involved in over 10,000 transactions.
  

• Many sellers have a presence across multiple deep web marketplaces, and oftentimes point to their profiles on different platforms as a way to further establish credibility.
  

• The community moderates sellers beyond the eBay-style reviews. A lot of marketplaces have separate community forums where users review sellers and products.
  

• A quick way for new sellers to establish credibility is to get reviewed by these community members.
  

• These forums often have established members, to whom sellers frequently send review samples.
  

• Sellers oftentimes link to these reviews as social proof, which are often rich in detail about the quality of the product (with pictures!), the seller, the packaging (good/bad stealth), speed, etc.
  

Flow of funds

• You are given a bitcoin public key on to which you must deposit funds before making a purchase.
  

• You’d buy bitcoin at an exchange, and use a mixing/tumbling service to anonymize them for a small fee. The need for these services in illegal transactions is interesting, since Bitcoin is frequently antagonized for its anonymity.
  

• You are expected to trust the marketplace with holding your funds. Some users keep a balance on their account, while others only make a deposit when they intend to make a purchase.
  

• Funds show up in your account once the transaction has been confirmed in the blockchain multiple times.
  

• Once funds are in your account, checkout is familiar and straightforward.
  

Escrow

• Escrow is provided by the marketplace operator and it is paramount to their business model.
  

• Sometimes you’ll find two service tiers: standard escrow (admins are the judges) or multisig escrow.
  

• To finance this service (and make a profit), marketplaces charge a small fee.
  

• Some sellers are very well established and have stellar reputations. This affords them the privilege of skipping escrow.
  

• Oftentimes, sellers will give you a discount (up to 20%) if you skip escrow or finalize early for the benefit of getting paid upfront.
  

• You have some number of days (15-30) to finalize the order (at which point the funds are transferred to the seller) or dispute it, at which point the staff gets involved.
  

Shipping

• This was not really relevant for my purposes, so I’m not entirely sure how shipping works for drugs. But I did some reading and wanted to share the most creative (emphasis on creative) method for anonymously receiving a package:
  

• One user put down the address of his local post office as a shipping address instead of his home. As a recipient, instead of his name he submitted “Holder of Federal Reserve Note number #NNNNN”, #NNNNN being the serial number of a dollar bill in his possession. Apparently he went to the post office holding the bill, correctly identifying himself as the holder of that federal reserve note, and was given the package (which I can only assume contained drugs).
  

Network effects

• There are no data network effects in the platform. In fact, deep web marketplace operators want to hold on to as little data as possible, as the opposite increases their exposure to prosecution.
  

• The network effects are in the seller’s reputation across many different forums, marketplaces, and websites (including “clear” web services like Reddit).
  

• Brand and product drive defensibility. Because the popular sellers are present in all major marketplaces, users mostly make decisions based on product. When new users ask for recommendations, they are oftentimes sent to a particular marketplace because of its ease of use.
  

Lessons learned

• A seller’s brand and reputation are extremely important in a system where the intermediary (the marketplace) does not guarantee trust and safety.
  

• This is largely decentralized in deep web marketplaces, as vendors make sure their brand is spread across multiple websites and forums.
  

• Marketplaces come and go (or get seized by the FBI) but sellers need maintain their reputation.
  

• Marketplaces can extract value where they incur costs. Because Bitcoin transactions are a commodity, high take rates and complex fee structures are unsustainable business models. This leads marketplaces to become very thin layers between supply and demand, which commands much smaller transaction fees – as low as 2% – to finance the small set of crucial services (enforcing contracts).
  

• The network regulates itself with relatively little involvement from its administrator (if networks are like governments, this is similar to a very small libertarian one).
  

• Peer to peer commerce, with no intermediary, can work: it depends on the reputation of the supplier and the size of the discount.
  

There’s a lot to learn from these platforms as we continue to think about how the Blockchain and other new technologies might impact traditional business models. For example, marketplaces with cost structures that command high take rates are vulnerable to Bitcoin-driven business models with very low or non-existent transaction fees. It could be that what drives adoption of unbundled services is competition by lowering costs.
I’m also wondering how applications could build network effects while defaulting to decentralized open data through the Blockchain Application Stack. While deep web marketplaces don’t fit this model, periodically purging the database has similar implications to giving up control of your user’s information by using decentralized data stores. Perhaps the answer is to have the best product and user experience.
This brings about a very interesting set of questions for both entrepreneurs and investors.
How do you monetize a decentralized network? Is it SAAS on top of the network?
How can you build build network effects while relinquishing control of the data? Do you compete on product and user experience? Is that defensible?
We have some ideas, but no definitive answers.

how to access the deep web, deep web search engines, deep web tor, deep web como entrar, deep web search,