research-article

A survey of emerging approaches to spam filtering

Authors:

Godwin Caruana,

Maozhen LiAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 44, Issue 2

Article No.: 9, Pages 1 - 27

https://doi.org/10.1145/2089125.2089129

Published: 05 March 2008 Publication History

Abstract

From just an annoying characteristic of the electronic mail epoch, spam has evolved into an expensive resource and time-consuming problem. In this survey, we focus on emerging approaches to spam filtering built on recent developments in computing technologies. These include peer-to-peer computing, grid computing, semantic Web, and social networks. We also address a number of perspectives related to personalization and privacy in spam filtering. We conclude that, while important advancements have been made in spam filtering in recent years, high performance approaches remain to be explored due to the large scale of the problem.

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Index Terms

A survey of emerging approaches to spam filtering
1. Applied computing
  1. Document management and text processing
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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RAID'11: Proceedings of the 14th international conference on Recent Advances in Intrusion Detection

Twitter is one of the most visited sites in these days. Twitter spam, however, is constantly increasing. Since Twitter spam is different from traditional spam such as email and blog spam, conventional spam filtering methods are inappropriate to detect ...
A survey and experimental evaluation of image spam filtering techniques

In their arms race against developers of spam filters, spammers have recently introduced the image spam trick to make the analysis of emails' body text ineffective. It consists in embedding the spam message into an attached image, which is often ...

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 44, Issue 2

February 2012

132 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2089125

Issue’s Table of Contents

Copyright © 2008 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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Accepted: 01 July 2010

Revised: 01 March 2010

Received: 01 November 2009

Published: 05 March 2008

Published in CSUR Volume 44, Issue 2

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Zavrak SYilmaz S(2023)Email spam detection using hierarchical attention hybrid deep learning methodExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120977233:COnline publication date: 15-Dec-2023
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Lukumon GKlein M(2023)Crowd-sourced idea filtering with Bag of Lemons: the impact of the token budget sizeDECISION10.1007/s40622-023-00349-w50:2(205-219)Online publication date: 8-Jul-2023
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El-Hajj WHajj H(2022)An optimal approach for text feature selectionComputer Speech and Language10.1016/j.csl.2022.10136474:COnline publication date: 1-Jul-2022
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Mohammed MIbrahim DSalman A(2021)Adaptive intelligent learning approach based on visual anti-spam email model for multi-natural languageJournal of Intelligent Systems10.1515/jisys-2021-004530:1(774-792)Online publication date: 29-Jun-2021
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Rahman MHalder SUddin MAcharjee U(2021)An efficient hybrid system for anomaly detection in social networksCybersecurity10.1186/s42400-021-00074-w4:1Online publication date: 2-Mar-2021
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Nisar NRakesh NChhabra M(2021)Voting-Ensemble Classification for Email Spam Detection2021 International Conference on Communication information and Computing Technology (ICCICT)10.1109/ICCICT50803.2021.9510066(1-6)Online publication date: 25-Jun-2021
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Huang DJiang JZhou FOuyang S(2021)A distributed algorithm for graph semi-supervised learningPattern Recognition Letters10.1016/j.patrec.2021.07.021151:C(48-54)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1016/j.patrec.2021.07.021
Rao SVerma ABhatia T(2021)A review on social spam detectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115742186:COnline publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.115742
Roussillon BLoiseau P(2021)Scalable Optimal Classifiers for Adversarial Settings Under UncertaintyDecision and Game Theory for Security10.1007/978-3-030-90370-1_5(80-97)Online publication date: 25-Oct-2021
https://dl.acm.org/doi/10.1007/978-3-030-90370-1_5
Ma YXie TLi JMaciejewski R(2020)Explaining Vulnerabilities to Adversarial Machine Learning through Visual AnalyticsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.293463126:1(1075-1085)Online publication date: Jan-2020
https://doi.org/10.1109/TVCG.2019.2934631
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