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

By Ying Tan

Email has turn into an quintessential conversation device in everyday life. notwithstanding, excessive volumes of unsolicited mail waste assets, intrude with productiveness, and current critical threats to computing device method defense and private privateness. This booklet introduces learn on anti-spam ideas in accordance with the synthetic immune process (AIS) to spot and clear out junk mail. It offers a unmarried resource of all anti-spam types and algorithms in accordance with the AIS which have been proposed by way of the writer for the previous decade in a number of journals and conferences.

Inspired by way of the organic immune method, the AIS is an adaptive procedure according to theoretical immunology and saw immune features, rules, and types for challenge fixing. one of the number of anti-spam innovations, the AIS has been powerful and is changing into the most very important the way to clear out unsolicited mail. The publication additionally makes a speciality of a number of key subject matters concerning the AIS, including:

  • Extraction tools encouraged by means of a variety of immune principles
  • Construction methods in accordance with numerous focus equipment and models
  • Classifiers in keeping with immune chance theory
  • The immune-based dynamic updating algorithm
  • Implementing AIS-based unsolicited mail filtering systems

The e-book additionally contains numerous experiments and comparisons with state of the art anti-spam options to demonstrate the wonderful functionality AIS-based anti-spam techniques.

Anti-Spam options according to man made Immune System

supplies practitioners, researchers, and teachers a centralized resource of distinct info on effective types and algorithms of AIS-based anti-spam strategies. It additionally comprises the most up-tp-date details at the common achievements of anti-spam study and methods, outlining ideas for designing and utilizing spam-filtering models.

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Since the sending purposes of spam are similar and the sending behaviors have some similarities, some studies can filter spam by group from the perspective of similarity. , containing the same URL link), and filters spam by group according to the similarity. Through analyzing the characteristics of e-mails, it is found that there is a higher possibility for the spammers who appear in more than one group to send spam again. Ramachandran et al. [151] study the similarity of e-mail sending mode. They define sending modes according to the sending frequency of an IP address to d different domains in the period t and adopt a clustering analysis on behaviors according to the sending modes.

3 Image-Based Feature Extraction Approaches Besides text content, e-mails sometimes contain image information. In normal e-mails, attached images are generally daily life photos about portraits, landscapes, architectures, and others for daily communication in life and work; While in spam, images always contain advertising text information for the purpose of advertising and marketing [25,75]. , and a number of image-based feature extraction approaches have been proposed according to the significant differences between these two categories of images [16,24].

K-Nearest Neighbors: Sakkis et al. [157] have put into effect the k-nearest neighbors—kNN (a classical lazy learning method) in the scope of spam detection. They have studied the influence of domain (k), the characteristic dimension, and the practice set on the performance of the testing machine. The experiments have shown that the average performance and properties are better than naive Bayes. 3. Boosting trees: Schapire and Singer [162] are, for the first time, to apply this method in the area of text classification, which handles the problems of divisions of multiclass and multilabel through multi-base hypotheses.

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