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Counterterrorism and Open Source Intelligence pp 249–264Cite as

Detection of Illegitimate Emails Using Boosting Algorithm

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Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

In this paper, we report on experiments to detect illegitimate emails using boosting algorithm. We call an email illegitimate if it is not useful for the receiver or for the society. We have divided the problem into two major areas of illegitimate email detection: suspicious email detection and spam email detection. For our desired task, we have applied a boosting technique. With the use of boosting we can achieve high accuracy of traditional classification algorithms. When using boosting one has to choose a suitable weak learner as well as the number of boosting iterations. In this paper, we propose suitable weak learners and parameter settings for the boosting algorithm for the desired task. We have initially analyzed the problem using base learners. Then we have applied boosting algorithm with suitable weak learners and parameter settings such as the number of boosting iterations. We propose a Naive Bayes classifier as a suitable weak learner for the boosting algorithm. It achieves maximum performance with very few boosting iterations.

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Author information

Authors and Affiliations

  1. Counterterrorism Research Lab, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark

    Sarwat Nizamani, Nasrullah Memon & Uffe Kock Wiil

  2. University of Sindh, Jamshoro, Pakistan

    Sarwat Nizamani

  3. Hellenic American University, Manchester, NH, USA

    Nasrullah Memon

Authors
  1. Sarwat Nizamani
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  2. Nasrullah Memon
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  3. Uffe Kock Wiil
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Corresponding author

Correspondence to Sarwat Nizamani .

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Editors and Affiliations

  1. The Maersk McKinney Moller Institute, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark

    Uffe Kock Wiil

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Nizamani, S., Memon, N., Wiil, U.K. (2011). Detection of Illegitimate Emails Using Boosting Algorithm. In: Wiil, U.K. (eds) Counterterrorism and Open Source Intelligence. Lecture Notes in Social Networks. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0388-3_13

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  • DOI: https://doi.org/10.1007/978-3-7091-0388-3_13

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  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-7091-0387-6

  • Online ISBN: 978-3-7091-0388-3

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