Spam-detection systems based on traditional methods have several obvious disadvantages like low detection rate, necessity of regularudknowledge bases’ updates, impersonal filtering rules. New intelligent methods for spam detection, which use statistical and machineudlearning algorithms, solve these problems successfully. But these methods are not widespread in spam filtering for enterprise-level mailudservers, because of their high resources consumption and insufficient accuracy regarding false-positive errors. The developed solutionudoffers precise and fast algorithm. Its classification quality is better than the quality of Naïve-Bayes method that is the most widespreadudmachine learning method now. The problem of time efficiency that is typical for all learning based methods for spam filtering is solvedudusing multi-agent architecture. It allows easy system scaling and building unified corporate spam detection system based on heterogeneousudenterprise mail systems. Pilot program implementation and its experimental evaluation for standard data sets and for real mail flows haveuddemonstrated that our approach outperforms existing learning and traditional spam filtering methods. That allows considering it as audpromising platform for constructing enterprise spam filtering systems.
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