research-article

Spam Filtering: an Active Learning Approach using Incremental Clustering

Authors:

Kleanthi Georgala,

Aris Kosmopoulos,

George PaliourasAuthors Info & Claims

WIMS '14: Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14)

Article No.: 23, Pages 1 - 12

https://doi.org/10.1145/2611040.2611059

Published: 02 June 2014 Publication History

Abstract

This paper introduces a method that deals with unwanted mail messages by combining active learning with incremental clustering. The proposed approach is motivated by the fact that the user cannot provide the correct category for all received messages. The email messages are divided into chronological batches (e.g. one per day). The user is asked to give the correct categories (labels) for the messages of the first batch and from then on the proposed algorithm decides when to ask for a new label, based on a clustering of the messages that is incrementally updated. We test different variants of the algorithm on a number of different datasets and show that it achieves very good results with only 2% of all email messages labelled by the user.

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Cited By

Chen YHuang LWang CFu MHuang SHuang JTan YYan C(2023)Adversarial Spam Detector With Character Similarity NetworkIEEE Transactions on Industrial Informatics10.1109/TII.2022.317772619:3(2541-2551)Online publication date: Mar-2023
https://doi.org/10.1109/TII.2022.3177726
Althobaiti KWolters MAlsufyani NVaniea K(2023)Using Clustering Algorithms to Automatically Identify Phishing CampaignsIEEE Access10.1109/ACCESS.2023.331081011(96502-96513)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3310810
Hrehova SKnapcikova L(2022)The Study of Machine Learning Assisted the Design of Selected Composites PropertiesApplied Sciences10.3390/app12211086312:21(10863)Online publication date: 26-Oct-2022
https://doi.org/10.3390/app122110863
Show More Cited By

Index Terms

Spam Filtering: an Active Learning Approach using Incremental Clustering
1. Information systems
  1. Information retrieval
  2. Information storage systems

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WIMS '14: Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14)

June 2014

506 pages

ISBN:9781450325387

DOI:10.1145/2611040

Copyright © 2014 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 the author(s) 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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Aristotle University of Thessaloniki

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Published: 02 June 2014

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WIMS '14

WIMS '14: 4th International Conference on Web Intelligence, Mining and Semantics

June 2 - 4, 2014

Thessaloniki, Greece

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WIMS '14 Paper Acceptance Rate 41 of 90 submissions, 46%;

Overall Acceptance Rate 140 of 278 submissions, 50%

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Cited By

Chen YHuang LWang CFu MHuang SHuang JTan YYan C(2023)Adversarial Spam Detector With Character Similarity NetworkIEEE Transactions on Industrial Informatics10.1109/TII.2022.317772619:3(2541-2551)Online publication date: Mar-2023
https://doi.org/10.1109/TII.2022.3177726
Althobaiti KWolters MAlsufyani NVaniea K(2023)Using Clustering Algorithms to Automatically Identify Phishing CampaignsIEEE Access10.1109/ACCESS.2023.331081011(96502-96513)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3310810
Hrehova SKnapcikova L(2022)The Study of Machine Learning Assisted the Design of Selected Composites PropertiesApplied Sciences10.3390/app12211086312:21(10863)Online publication date: 26-Oct-2022
https://doi.org/10.3390/app122110863
Salloum SGaber TVadera SShaalan K(2022)A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing TechniquesIEEE Access10.1109/ACCESS.2022.318308310(65703-65727)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3183083
Gupta BTewari ACvitić IPeraković DChang X(2021)Artificial intelligence empowered emails classifier for Internet of Things based systems in industry 4.0Wireless Networks10.1007/s11276-021-02619-wOnline publication date: 12-Apr-2021
https://doi.org/10.1007/s11276-021-02619-w
Nagao KNagao K(2019)Discussion Data AnalyticsArtificial Intelligence Accelerates Human Learning10.1007/978-981-13-6175-3_2(19-56)Online publication date: 3-Feb-2019
https://doi.org/10.1007/978-981-13-6175-3_2
Chan PHe ZLi HHsu C(2017)Data sanitization against adversarial label contamination based on data complexityInternational Journal of Machine Learning and Cybernetics10.1007/s13042-016-0629-59:6(1039-1052)Online publication date: 24-Jan-2017
https://doi.org/10.1007/s13042-016-0629-5
Gupta BTewari AJain AAgrawal D(2017)Fighting against phishing attacksNeural Computing and Applications10.1007/s00521-016-2275-y28:12(3629-3654)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s00521-016-2275-y
Feng LWang YZuo W(2015)Quick online spam classification method based on active and incremental learningJournal of Intelligent & Fuzzy Systems10.3233/IFS-15170730:1(17-27)Online publication date: 21-Aug-2015
https://doi.org/10.3233/IFS-151707
Sipahi DDalklç GÖzcanhan M(2015)Detecting spam through their Sender Policy Framework recordsSecurity and Communication Networks10.1002/sec.12808:18(3555-3563)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1002/sec.1280

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