Channels ▼


Algorithm Helps ICANN Manage Top-level Domains

At the request of ICANN, Paul E. Black, a computer scientist at the National Institute of Standards and Technology (NIST) has developed an algorithm that may guide applicants in proposing new top-level domains. As new top-level domains are added to the familiar .com, .info, and .net, the algorithm checks whether the newly proposed name is confusingly similar to existing ones by looking for visual likenesses in its appearance. Having visually distinct top-level domain names may help avoid confusion in navigating the ever-expanding Internet and combat fraud, by reducing the potential to create malicious look-alikes: .C0M with a zero instead of .COM, for instance.

Later this year, the Internet Corporation for Assigned Names and Numbers (ICANN) plans to launch the process for proposing a new round of "generic" top-level domains (gTLDs), strings such as .net, .gov, and .org meant to indicate organizations or interests. In preparing for newly proposed gTLDs, ICANN reached out to various algorithm developers, including Paul Black, as among those engaged to "provide an open, objective, and predictable mechanism for assessing the degree of visual confusion" in gTLDs.


To make its assessments, the algorithm rates the degree of similarity between pairs of alpha-numeric characters. Some pairs, such as the numeral "1" and its dead-ringer, the lowercase letter "l," are assigned the highest scores for visual similarity while other pairs, such as "h" and "n", are given lower scores. The algorithm takes other considerations into account, for example how certain pairs of letters, like "c" and "l," can join to look like a third letter ("d"), as in the case of "close" and "dose." Employing these scores and considerations, the algorithm computes the "cost" of transforming one string of characters into another, such as "opel" into "apple." Lower cost means higher visual similarity. The algorithm then adjusts for the relative lengths of the two strings (different lengths increase their distinctiveness) and converts the final cost into a percent similarity.


Related Reading

More Insights

Currently we allow the following HTML tags in comments:

Single tags


<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags


<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

