A machine learning approach to server-side anti-spam e-mail filtering
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...
Saved in:
| Date: | 2006 |
|---|---|
| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
| Published: |
Інститут програмних систем НАН України
2006
|
| Subjects: | |
| Online Access: | https://nasplib.isofts.kiev.ua/handle/123456789/1564 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Journal Title: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Cite this: | 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. |
Institution
Digital Library of Periodicals of National Academy of Sciences of Ukraine| id |
nasplib_isofts_kiev_ua-123456789-1564 |
|---|---|
| record_format |
dspace |
| spelling |
Mashechkin, I. Petrovskiy, M. Rozinkin, A. Gerasimov, S. 2008-08-22T16:59:39Z 2008-08-22T16:59:39Z 2006 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. 1727-4907 https://nasplib.isofts.kiev.ua/handle/123456789/1564 004.75 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. en Інститут програмних систем НАН України Паралельне програмування. Розподілені системи і мережі A machine learning approach to server-side anti-spam e-mail filtering Article published earlier |
| institution |
Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| collection |
DSpace DC |
| title |
A machine learning approach to server-side anti-spam e-mail filtering |
| spellingShingle |
A machine learning approach to server-side anti-spam e-mail filtering Mashechkin, I. Petrovskiy, M. Rozinkin, A. Gerasimov, S. Паралельне програмування. Розподілені системи і мережі |
| title_short |
A machine learning approach to server-side anti-spam e-mail filtering |
| title_full |
A machine learning approach to server-side anti-spam e-mail filtering |
| title_fullStr |
A machine learning approach to server-side anti-spam e-mail filtering |
| title_full_unstemmed |
A machine learning approach to server-side anti-spam e-mail filtering |
| title_sort |
machine learning approach to server-side anti-spam e-mail filtering |
| author |
Mashechkin, I. Petrovskiy, M. Rozinkin, A. Gerasimov, S. |
| author_facet |
Mashechkin, I. Petrovskiy, M. Rozinkin, A. Gerasimov, S. |
| topic |
Паралельне програмування. Розподілені системи і мережі |
| topic_facet |
Паралельне програмування. Розподілені системи і мережі |
| publishDate |
2006 |
| language |
English |
| publisher |
Інститут програмних систем НАН України |
| format |
Article |
| description |
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.
|
| issn |
1727-4907 |
| url |
https://nasplib.isofts.kiev.ua/handle/123456789/1564 |
| citation_txt |
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. |
| work_keys_str_mv |
AT mashechkini amachinelearningapproachtoserversideantispamemailfiltering AT petrovskiym amachinelearningapproachtoserversideantispamemailfiltering AT rozinkina amachinelearningapproachtoserversideantispamemailfiltering AT gerasimovs amachinelearningapproachtoserversideantispamemailfiltering AT mashechkini machinelearningapproachtoserversideantispamemailfiltering AT petrovskiym machinelearningapproachtoserversideantispamemailfiltering AT rozinkina machinelearningapproachtoserversideantispamemailfiltering AT gerasimovs machinelearningapproachtoserversideantispamemailfiltering |
| first_indexed |
2025-12-07T15:15:16Z |
| last_indexed |
2025-12-07T15:15:16Z |
| _version_ |
1850863011221209088 |