Spam Detection from Big Data based on Evolutionary Data Mining Systems

Document Type : Original Article

Authors

1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

2 Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran

Abstract

News releases and users' ability to discuss events, events, and writing personalities and environments are services that provide opportunities for new types of spam and spammers. For example, popular topics and topics that involve the most discussions can be an opportunity to create traffic, visits, and sources of income. When something happens, thousands of users write about it, send text and quickly become the subject of discussion. These topics are targeted by spammers, because their writings contain the common words used in popular discussions. Often there are links in spam that direct users to websites that are not related to the topic, and since these URLs are shortened, it's difficult for users to log in. This type of spams can reduce the value and efficiency of instantaneous search services, and users of these services refer to materials that do not contain links to the searcher, so a method for identifying spammers should be found. Methods available to deal with spammers can be included in three categories which contain detection-based approach, prevention-based approach, and degradation-based approach that this research uses is a detection approach. Hence, this research uses a smart method that initially enters large data into the program, then a feature extraction based on the genetic algorithm is performed. In the next step, the classification of data in order to detect spam is done using the combined method of self-organized mapping neural network and probabilistic neural network with the support vector machine core as a radial basis function.

Keywords

References

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Transactions on Machine Intelligence
Volume 1, Issue 1
December 2018
Pages 1-9

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History

  • Receive Date: 29 March 2018
  • Revise Date: 14 April 2018
  • Accept Date: 11 June 2018

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