classification of textual e-mail spam using data mining techniques

Clicks: 140
ID: 182151
2011
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Abstract
A new method for clustering of spam messages collected in bases of antispam system is offered. The genetic algorithm is developed for solving clustering problems. The objective function is a maximization of similarity between messages in clusters, which is defined by k-nearest neighbor algorithm. Application of genetic algorithm for solving constrained problems faces the problem of constant support of chromosomes which reduces convergence process. Therefore, for acceleration of convergence of genetic algorithm, a penalty function that prevents occurrence of infeasible chromosomes at ranging of values of function of fitness is used. After classification, knowledge extraction is applied in order to get information about classes. Multidocument summarization method is used to get the information portrait of each cluster of spam messages. Classifying and parametrizing spam templates, it will be also possible to define the thematic dependence from geographical dependence (e.g., what subjects prevail in spam messages sent from certain countries). Thus, the offered system will be capable to reveal purposeful information attacks if those occur. Analyzing origins of the spam messages from collection, it is possible to define and solve the organized social networks of spammers.
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alguliev2011appliedclassification Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Rasim M. Alguliev;Ramiz M. Aliguliyev;Saadat A. Nazirova
Journal journal of evidence-based complementary & alternative medicine
Year 2011
DOI
10.1155/2011/416308
URL
http://dx.doi.org/10.1155/2011/416308
Keywords
computer science electronic computers

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