Machine learning methods for analyzing user behavior when accessing text data in information security problems
| Autor: | M. I. Petrovskii, Igor Mashechkin, Dmitry V. Tsarev |
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| Rok vydání: | 2016 |
| Předmět: |
Control and Optimization
Computer science business.industry Subject (documents) 02 engineering and technology Information security Machine learning computer.software_genre 01 natural sciences Non-negative matrix factorization Human-Computer Interaction Data set 010104 statistics & probability Computational Mathematics 0202 electrical engineering electronic engineering information engineering Subject areas 020201 artificial intelligence & image processing Orthonormal basis Data mining Artificial intelligence 0101 mathematics business computer |
| Zdroj: | Moscow University Computational Mathematics and Cybernetics. 40:179-184 |
| ISSN: | 1934-8428 0278-6419 |
| Popis: | A new method for detecting user access to irrelevant documents based on estimating the document text membership in typical subject areas of the analyzed user is proposed. The typical subject areas are formed using subject area modeling implemented via orthonormal nonnegative matrix factorization. An experimental study with real corporate correspondence formed from an Enron data set demonstrates the high classification accuracy of the proposed method, compared to traditional approaches. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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