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-|style="text-align:left;"|'''·''' [[Escan/english/eScan-Articles|<font size=1.5 color="blue" align="left">Articles</font>]]&nbsp;&nbsp;'''·''' [[Escan/english/eScan-FAQ|<font size=1.5 color="blue">FAQ</font>]]&nbsp;&nbsp;'''·''' [[Escan/english/eScan-Troubleshooting|<font size=1.5 color="blue">Troubleshooting</font>]]+|style="text-align:left;"|'''·''' [[Escan/english/FAQ-eScan|<font size=1.5 color="blue"
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-NILP works on the basis of Artificial Intelligence where in, it learns user’s behavior and then takes an informed decision in the background and help identify and classify mails on behalf of the users whether the mails are spam or ham.+This is a revolutionary technology from MicroWorld that works on the principles of Artificial Intelligence to create an adaptive mechanism in Spam and Phishing Control. This technology analyzes each email according to the Behavioral Patterns of the user and then takes an informed decision there after. NILP has a self learning mechanism apart from incorporating regular research feeds from MicroWorld's Server.
 + 
 + 
 +__TOC__
 +
 + 
 +=='''Description'''==
 + 
 +'''How NILP Works?'''
 + 
 +NILP uses Bayesian Filtering and works on the principles of Artificial Intelligence (AI). It has self learning capabilities and uses an adaptive mechanism to categorize e mails based on the behavioral pattern of the user. NILP updates itself by using regular research feeds from MicroWorld servers. Whenever a new e mail arrives, NILP analyzes it based on the accumulated learning, and classifies it as ham or spam.
 + 
 +NILP also maintains a database containing the DNA imprints of millions of SPAM e mails, which it keeps updating continuously. It uses the existing DNA imprints in the database to reverse its learning and determine whether a given e mail is ham or spam. In this way, the NILP technology protects the user’s inbox from spam and phishing e-mails.
 + 
 + 
 +__TOC__
 + 
 + 
 +=='''Benefits of NILP'''==
 + 
 +The following are some of the benefits of the NILP technology.
 + 
 +* It prevents spam and phishing e mails from reaching the user’s inbox.
 +* It categorizes e mails into spam or ham based on the behavioral pattern of the user.
 + 
 +__TOC__
 + 
 +=='''Summary'''==
 + 
 +In this section, you read about NILP and how it controls spam e mails.
 + 
 + 
 +Return to [[Escan/english/Technologies| <font color="blue">Technologies]]

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· eScan  · MailScan  · Technologies   · Technical Info  · Security Awareness  · User Guides
Non Intrusive Learning Pattern (NILP) Technology

Contents


Overview

This is a revolutionary technology from MicroWorld that works on the principles of Artificial Intelligence to create an adaptive mechanism in Spam and Phishing Control. This technology analyzes each email according to the Behavioral Patterns of the user and then takes an informed decision there after. NILP has a self learning mechanism apart from incorporating regular research feeds from MicroWorld's Server.



Description

How NILP Works?

NILP uses Bayesian Filtering and works on the principles of Artificial Intelligence (AI). It has self learning capabilities and uses an adaptive mechanism to categorize e mails based on the behavioral pattern of the user. NILP updates itself by using regular research feeds from MicroWorld servers. Whenever a new e mail arrives, NILP analyzes it based on the accumulated learning, and classifies it as ham or spam.

NILP also maintains a database containing the DNA imprints of millions of SPAM e mails, which it keeps updating continuously. It uses the existing DNA imprints in the database to reverse its learning and determine whether a given e mail is ham or spam. In this way, the NILP technology protects the user’s inbox from spam and phishing e-mails.



Benefits of NILP

The following are some of the benefits of the NILP technology.

  • It prevents spam and phishing e mails from reaching the user’s inbox.
  • It categorizes e mails into spam or ham based on the behavioral pattern of the user.


Summary

In this section, you read about NILP and how it controls spam e mails.


Return to Technologies


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