Download e-book for kindle: Anti-spam techniques based on artificial immune system by Ying Tan

By Ying Tan

ISBN-10: 1498725198

ISBN-13: 9781498725194

Email has develop into an crucial communique device in way of life. in spite of the fact that, excessive volumes of unsolicited mail waste assets, intrude with productiveness, and current serious threats to desktop procedure safeguard and private privateness. This e-book introduces study on anti-spam strategies in keeping with the substitute immune method (AIS) to spot and filter out unsolicited mail. It offers a unmarried resource of all anti-spam versions and algorithms in line with the AIS which have been proposed by means of the writer for the prior decade in a number of journals and conferences.

Inspired via the organic immune method, the AIS is an adaptive procedure in response to theoretical immunology and saw immune features, rules, and types for challenge fixing. one of the number of anti-spam strategies, the AIS has been powerful and is changing into the most very important the way to filter out junk mail. The booklet additionally specializes in a number of key subject matters with regards to the AIS, including:

  • Extraction equipment encouraged by way of quite a few immune principles
  • Construction ways in response to numerous focus tools and models
  • Classifiers in keeping with immune probability theory
  • The immune-based dynamic updating algorithm
  • Implementing AIS-based junk mail filtering systems

The ebook additionally contains a number of experiments and comparisons with cutting-edge anti-spam options to demonstrate the wonderful functionality AIS-based anti-spam techniques.

Anti-Spam concepts in response to synthetic Immune System

offers practitioners, researchers, and lecturers a centralized resource of distinctive details on effective versions and algorithms of AIS-based anti-spam options. It additionally includes the most up-tp-date details at the basic achievements of anti-spam examine and ways, outlining suggestions for designing and using spam-filtering models.

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PU3: It contains 4139 e-mails, of which 1826 are spam. Contrast to PU1 and PU2, this dataset covers e-mails both in English and in other languages. Legitimate e-mails in this dataset were collected by the second author while spam were cited from other datasets. PUA: This one contains 1142 e-mails, 572 of which were spam. Like PU3, this dataset contains some e-mails in other languages and spam were from other datasets. Legitimate e-mails were collected by another colleague. Medlock [129] disposed and publicized another large-scale e-mail dataset called GenSpam.

4) Rs = ns→s + ns→l where, ns→s means the number of spam correctly spotted and categorized while ns→l means the number of spam mistaken as normal mails. 2. Spam precision: Spam precision can figure out the precision of measuring spam. It can figure out the rate of spam correctly spotted and categorized. It can also reflect the rate of normal e-mails mistaken as spam. The higher the spam precision is, the less the number of normal e-mails mistaken as spam is. 5) Ps = ns→s + nl→s where, nl→s means the number of normal e-mails that are mistaken as spam.

3 Character Edge Features On the basis of extracting edge of character vertically, Aradhye et al. [15] divide the image into text area and non-text area by calculating the similarity of character edge in each region and merging the similar regions. After the division, features of each image are constructed by calculating the size of text area in each image as well as Anti-Spam Technologies ■ 13 the corresponding color saturation and color unevenness in text and non-text areas, respectively. Finally, the feature vector of an e-mail is achieved by calculating the weighted sum of related features of all images included in the e-mail according to the acreage of each image and support vector machine (SVM) [195] is employed for classification.

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Anti-spam techniques based on artificial immune system by Ying Tan


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