A machine learning approach to server-side anti-spam e-mail filtering

• Дата: 2006
• Автори: Mashechkin, I., Petrovskiy, M., Rozinkin, A., Gerasimov, S.
• Формат: Стаття
• Мова: Англійська
• Опубліковано: Інститут програмних систем НАН України 2006
• Теми: Паралельне програмування. Розподілені системи і мережі
• Онлайн доступ: https://nasplib.isofts.kiev.ua/handle/123456789/1564
• Назва журналу: Digital Library of Periodicals of National Academy of Sciences of Ukraine
• Цитувати: A machine learning approach to server-side anti-spam e-mail filtering / I. Mashechkin, M. Petrovskiy, A. Rozinkin, S. Gerasimov // Проблеми програмування. — 2006. — N 2-3. — С. 216-220. — Бібліогр.: 13 назв. — англ.

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