Article

Free access

An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages

Authors:

Ion Androutsopoulos,

Konstantinos V. Chandrinos,

Constantine D. SpyropoulosAuthors Info & Claims

SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval

Pages 160 - 167

https://doi.org/10.1145/345508.345569

Published: 01 July 2000 Publication History

Abstract

The growing problem of unsolicited bulk e-mail, also known as “spam”, has generated a need for reliable anti-spam e-mail filters. Filters of this type have so far been based mostly on manually constructed keyword patterns. An alternative approach has recently been proposed, whereby a Naive Bayesian classifier is trained automatically to detect spam messages. We test this approach on a large collection of personal e-mail messages, which we make publicly available in “encrypted” form contributing towards standard benchmarks. We introduce appropriate cost-sensitive measures, investigating at the same time the effect of attribute-set size, training-corpus size, lemmatization, and stop lists, issues that have not been explored in previous experiments. Finally, the Naive Bayesian filter is compared, in terms of performance, to a filter that uses keyword patterns, and which is part of a widely used e-mail reader.

References

[1]

C. Apte and F. Damerau. Automated Learning of Decision Rules for Text Categorization. ACM Transactions on Information Systems, 12(3):233-251, 1994.

Digital Library

[2]

W.W. Cohen. Learning Rules that Classify E-Mail. In Proc. of the AAAI Spring Symposium on Machine Learning in Information Access, Stanford, California, 1996.

[3]

L.F. Cranor and B.A. LaMacchia. Spare Communications of the ACM, 41(8):74-83, 1998.

Digital Library

[4]

H. Cunningham, Y. Wilks and R. Gaizauskas. GATE - a General Architecture for Text Engineering. In Proc. of the 16 th International Conference on Computational Linguistics, Copenhagen, Denmark, 1996.

Digital Library

[5]

I. Dagan, Y. Karov and D. Roth. Mistake-Driven Learning in Text Categorization. In Proc. of the 2 na Conference on Empirical Methods in Natural Language Processing, pp. 55- 63, Providence, Rhode Island, 1997.

[6]

P. Domingos and M. Pazzani. Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. In Proc. o f the 13 th International Conference on Machine Learning, pp. 105-112, Bari, Italy, 1996.

[7]

R.O. Duda and P.E. Hart. Bayes Decision Theory. Chapter 2 in Pattern Classification and Scene Analysis, pp. 10-43. John Wiley, 1973.

[8]

D. Forsyth. Finding Naked People. In Proc. of the 4 th European Conference on Computer Vision, Cambridge, England, 1996.

Digital Library

[9]

K.T. Frantzi. Automatic Recognition of Multi-Word Terms. PhD Thesis, Manchester Metropolitan University, England, 1998.

[10]

N. Friedman, D. Geiger and M. Goldszmidt. Bayesian Network Classifiers. Machine Learning, 29(2/3):131-163, 1997.

Digital Library

[11]

C.L. Green and P. Edwards. Using Machine Learning to Enhance Software Tools for lnternet Information Management. In Proc. o f the AAAI Workshop on Internet-Based Information Systems, pp. 48-55, Portland, Oregon, 1996.

[12]

R.J. Hall. How to Avoid Unwanted Email. Communications o f the ACM, 41 (3):88-95, 1998.

Digital Library

[13]

N. Kushmerick. Learning to Remove Internet Advertisements. In Proc. of the 3 "a International Conference on Autonomous Agents, pp. 175-181, Seattle, Washington, 1999.

Digital Library

[14]

K. Lang. Newsweeder: Learning to Filter Netnews. In Proc. of the 12 th International Conference on Machine Learning, pp. 331-339, Stanford, California, 1995.

[15]

P. Langley, I. Wayne and K. Thompson. An Analysis of Bayesian Classifiers. In Proc. o f the 10 h National Conference on Artificial Intelligence, pp. 223-228, San Jose, California, 1992.

Digital Library

[16]

D. Lewis. Feature Selection and Feature Extraction for Text Categorization. In Proc. o f the DARPA Workshop on Speech and Natural Language, pp. 212-217, Harriman, New York, 1992.

Digital Library

[17]

D. Lewis. Training Algorithms for Linear Text Classifiers. In Proe. of the 19 th Annual International ACM- SIGIR Conference on Research and Development in Information Retrieval, pp. 298-306, Konstanz, Germany, 1996.

Digital Library

[18]

D. Lewis and K.A. Knowles. Threading Electronic Mail: A Preliminary Study. Information Processing and Management, 33(2):209-217, 1997.

Digital Library

[19]

H. Li and K. Yamanishi. Document Classification Using a Finite Mixture Model. In Proc. of the 35 th Annual Meeting of the ACL and the 8 th Conference of the EACL, pp, 39-47, Madrid, Spain, 1997.

Digital Library

[20]

T.M. Mitchell. Bayesian Learning. Chapter 6 in Machine Learning, pp. 154-200. McGraw-Hill, 1997.

Digital Library

[21]

T.R. Payne and P. Edwards. Interface Agents that Learn: An Investigation of Learning Issues in a Mail Agent Interface. Applied Artificial Intelligence, 11 (1): 1-32, 1997.

[22]

E. Riloff and W. Lehnert. Information Extraction as a Basis for High-Precision Text Classification. ACM Transactions on Information Systems, 12(3):296-333, 1994.

Digital Library

[23]

M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz. A Bayesian Approach to Filtering Junk E-Mail. In Learning for Text Categorization - Papers fi'om the AAA1 Workshop, pp. 55-62, Madison Wisconsin. AAAI Technical Report WS-98- 05, 1998.

[24]

G. Saiton and M.J. MeGill. Introduction to Modern Information Retrieval. McGraw-Hill, 1983.

Digital Library

[25]

E. Spertus. Smokey: Automatic Recognition of Hostile Messages. In Proc. of the 14 th National Conference on AI and th the 9 Conference on Innovative Applications of A1, pp. 1058- 1065, Providence, Rhode Island, 1997.

Digital Library

Cited By

G GT VV S(2025)Outsmarting Phishers: A Comparative Analysis of Machine Learning TechniquesJournal of Information Technology and Digital World10.36548/jitdw.2024.4.0036:4(347-361)Online publication date: Jan-2025
https://doi.org/10.36548/jitdw.2024.4.003
Allam HMakubvure LGyamfi BGraham KAkinwolere K(2025)Text Classification: How Machine Learning Is Revolutionizing Text CategorizationInformation10.3390/info1602013016:2(130)Online publication date: 10-Feb-2025
https://doi.org/10.3390/info16020130
Kundra SKant RSingh SSaini RGupta BManukonda VJain V(2024)Amalgamating an intelligent variant of the gravitational search algorithm with decision trees for email spam detectionInternational Journal of Modern Physics C10.1142/S012918312450191236:03Online publication date: 6-Aug-2024
https://doi.org/10.1142/S0129183124501912
Show More Cited By

Index Terms

An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. World Wide Web
    1. Web applications
      1. Internet communications tools
        Email

Recommendations

Using E-mail Authentication and Disposable E-mail Addressing for Filtering Spam
ISPAN '09: Proceedings of the 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks

The number of spam mails has spread rapidly in recent years. Currently, the most common spam filtering solutions include blacklisting and content filtering, as well as the Bayesian approach, which uses a Bayesian filter to analyze mail content to ...
Spam Filtering using Spam Mail Communities
SAINT '05: Proceedings of the The 2005 Symposium on Applications and the Internet

We might have heard quite a few people say on seeing some new mails in their inboxes, "Oh! That spam again". People who observe the kind of spam messages that they receive would perhaps be able to classify similar spam mails into communities. Such ...
Effective spam filtering: A single-class learning and ensemble approach

The annoyance of spam emails increasingly plagues both individuals and organizations. In response, most of prior research investigates spam filtering as a classical text categorization task, in which training examples must include both spam (positive ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval

July 2000

396 pages

ISBN:1581132263

DOI:10.1145/345508

Chairmen:
Emmanuel Yannakoudakis
Athens Univ. of Economics and Business, Greece
,
Nicholas J. Belkin
Rutgers Univ.
,
Mun-Kew Leong
Kent Ridge Digital Labs
,
Peter Ingwersen
Royal School of Library and Information Science

Copyright © 2000 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Greek Com Soc: Greek Computer Society
SIGIR: ACM Special Interest Group on Information Retrieval
Athens U of Econ & Business: Athens University of Economics and Business

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 2000

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

SIGIR00

Sponsor:

Greek Com Soc
SIGIR
Athens U of Econ & Business

SIGIR00: 23rd ACM International SIGIR Conference on Research and Development in Information Retrieval

July 24 - 28, 2000

Athens, Greece

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

261
Total Citations
View Citations
719
Total Downloads

Downloads (Last 12 months)257
Downloads (Last 6 weeks)62

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

G GT VV S(2025)Outsmarting Phishers: A Comparative Analysis of Machine Learning TechniquesJournal of Information Technology and Digital World10.36548/jitdw.2024.4.0036:4(347-361)Online publication date: Jan-2025
https://doi.org/10.36548/jitdw.2024.4.003
Allam HMakubvure LGyamfi BGraham KAkinwolere K(2025)Text Classification: How Machine Learning Is Revolutionizing Text CategorizationInformation10.3390/info1602013016:2(130)Online publication date: 10-Feb-2025
https://doi.org/10.3390/info16020130
Kundra SKant RSingh SSaini RGupta BManukonda VJain V(2024)Amalgamating an intelligent variant of the gravitational search algorithm with decision trees for email spam detectionInternational Journal of Modern Physics C10.1142/S012918312450191236:03Online publication date: 6-Aug-2024
https://doi.org/10.1142/S0129183124501912
Popova SNenov HStoyanova D(2024)Spam Detection System Based on Hybrid Scoring2024 International Conference Automatics and Informatics (ICAI)10.1109/ICAI63388.2024.10851565(120-125)Online publication date: 10-Oct-2024
https://doi.org/10.1109/ICAI63388.2024.10851565
Campit J(2023)Analyzing Customer Satisfaction using Support Vector Machine and Naive Bayes Utilizing Filipino TextWSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT10.37394/232015.2023.19.5019(514-524)Online publication date: 6-Jun-2023
https://doi.org/10.37394/232015.2023.19.50
Nguyen T(2023)Topic based document modeling for information filteringCTU Journal of Innovation and Sustainable Development10.22144/ctujoisd.2023.04015:ISDS(102-109)Online publication date: 16-Oct-2023
https://doi.org/10.22144/ctujoisd.2023.040
Nosrati VRahmani MJolfaei ASeifollahi S(2023)A Weak-Region Enhanced Bayesian Classification for Spam Content-Based FilteringACM Transactions on Asian and Low-Resource Language Information Processing10.1145/351042022:3(1-18)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1145/3510420
Kumar IDutta K(2023)Rhyme detection of Hindi and Rajasthani Poems using Statistical-Based Methods2023 IEEE International Conference on Contemporary Computing and Communications (InC4)10.1109/InC457730.2023.10262983(1-5)Online publication date: 21-Apr-2023
https://doi.org/10.1109/InC457730.2023.10262983
Wang RCao NGuo YJi SKumar S(2022)A Comparative Analysis of Fraudulent Recruitment Advertisement Detection Methods in the IoT EnvironmentJournal of Sensors10.1155/2022/45835122022(1-11)Online publication date: 8-Nov-2022
https://doi.org/10.1155/2022/4583512
Frangos MO’Shea F(2022)A graph-based approach to client relationship management in fund administrationMachine Learning with Applications10.1016/j.mlwa.2022.10043310(100433)Online publication date: Dec-2022
https://doi.org/10.1016/j.mlwa.2022.100433
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten