Classification of Textual E-Mail Spam Using Data Mining Techniques
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. Ap...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2011/416308 |
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| _version_ | 1850110841635995648 |
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| author | Rasim M. Alguliev Ramiz M. Aliguliyev Saadat A. Nazirova |
| author_facet | Rasim M. Alguliev Ramiz M. Aliguliyev Saadat A. Nazirova |
| author_sort | Rasim M. Alguliev |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-44ecb6558e1d4f7bba19b1522ce39686 |
| institution | OA Journals |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-44ecb6558e1d4f7bba19b1522ce396862025-08-20T02:37:46ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322011-01-01201110.1155/2011/416308416308Classification of Textual E-Mail Spam Using Data Mining TechniquesRasim M. Alguliev0Ramiz M. Aliguliyev1Saadat A. Nazirova2Institute of Information Technology of Azerbaijan National Academy of Sciences, 9 F. Agayev Street, Baku 1141, AzerbaijanInstitute of Information Technology of Azerbaijan National Academy of Sciences, 9 F. Agayev Street, Baku 1141, AzerbaijanInstitute of Information Technology of Azerbaijan National Academy of Sciences, 9 F. Agayev Street, Baku 1141, AzerbaijanA 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.http://dx.doi.org/10.1155/2011/416308 |
| spellingShingle | Rasim M. Alguliev Ramiz M. Aliguliyev Saadat A. Nazirova Classification of Textual E-Mail Spam Using Data Mining Techniques Applied Computational Intelligence and Soft Computing |
| title | Classification of Textual E-Mail Spam Using Data Mining Techniques |
| title_full | Classification of Textual E-Mail Spam Using Data Mining Techniques |
| title_fullStr | Classification of Textual E-Mail Spam Using Data Mining Techniques |
| title_full_unstemmed | Classification of Textual E-Mail Spam Using Data Mining Techniques |
| title_short | Classification of Textual E-Mail Spam Using Data Mining Techniques |
| title_sort | classification of textual e mail spam using data mining techniques |
| url | http://dx.doi.org/10.1155/2011/416308 |
| work_keys_str_mv | AT rasimmalguliev classificationoftextualemailspamusingdataminingtechniques AT ramizmaliguliyev classificationoftextualemailspamusingdataminingtechniques AT saadatanazirova classificationoftextualemailspamusingdataminingtechniques |