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Analysis and comparison of fax spam detection algorithms

Published: 05 January 2017 Publication History

Abstract

Spam detection is one of the important problems in these days. Many spam detection methods were proposed, but fax spam detection is not popular. It not easy to directly use existing content-based spam detection methods for fax documents because the documents are processed as image rather than text. In this paper, we propose a fax spam detection framework which is based on keyword patterns by using an Optical Character Recognition (OCR) technique. To demonstrate how effective the proposed framework is, we analyze and compare three fax spam detection algorithms (rule based method, SVM based method, and naïve Bayesian based method) with 219 normal and 212 spam documents. Our recommendation is to use naïve Bayesian based method which is capable of achieving an accuracy of 92.49%.

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Cited By

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  • (2023)A Late Multi-modal Fusion Model for Detecting Hybrid Spam E-mailInternational Journal of Computer Theory and Engineering10.7763/IJCTE.2023.V15.133415:2(76-81)Online publication date: 2023
  • (2020)DeepCapture: Image Spam Detection Using Deep Learning and Data AugmentationInformation Security and Privacy10.1007/978-3-030-55304-3_24(461-475)Online publication date: 6-Aug-2020

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  1. Analysis and comparison of fax spam detection algorithms

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    cover image ACM Conferences
    IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication
    January 2017
    746 pages
    ISBN:9781450348881
    DOI:10.1145/3022227
    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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    Publication History

    Published: 05 January 2017

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    Author Tags

    1. SVM
    2. comparison
    3. detection
    4. fax spam
    5. naïve Bayesian
    6. rule based filtering

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    • Short-paper

    Funding Sources

    • National Research Foundation of Korea (NRF) by Korea government

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    IMCOM '17 Paper Acceptance Rate 113 of 366 submissions, 31%;
    Overall Acceptance Rate 213 of 621 submissions, 34%

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    View all
    • (2023)A Late Multi-modal Fusion Model for Detecting Hybrid Spam E-mailInternational Journal of Computer Theory and Engineering10.7763/IJCTE.2023.V15.133415:2(76-81)Online publication date: 2023
    • (2020)DeepCapture: Image Spam Detection Using Deep Learning and Data AugmentationInformation Security and Privacy10.1007/978-3-030-55304-3_24(461-475)Online publication date: 6-Aug-2020

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