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Computational Intelligence in Digital Forensics: Forensic Investigation and Applications pp 413–428Cite as

Data Mining Methods Applied to a Digital Forensics Task for Supervised Machine Learning

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Part of the Studies in Computational Intelligence book series (SCI,volume 555)

Abstract

Digital forensics research includes several stages. Once we have collected the data the last goal is to obtain a model in order to predict the output with unseen data. We focus on supervised machine learning techniques. This chapter performs an experimental study on a forensics data task for multi-class classification including several types of methods such as decision trees, bayes classifiers, based on rules, artificial neural networks and based on nearest neighbors. The classifiers have been evaluated with two performance measures: accuracy and Cohen’s kappa. The followed experimental design has been a 4-fold cross validation with thirty repetitions for non-deterministic algorithms in order to obtain reliable results, averaging the results from 120 runs. A statistical analysis has been conducted in order to compare each pair of algorithms by means of t-tests using both the accuracy and Cohen’s kappa metrics.

Keywords

  • Digital forensics
  • Glass evidence
  • Data mining
  • Supervised machine learning
  • Classification model

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  • DOI: 10.1007/978-3-319-05885-6_17
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Author information

Authors and Affiliations

  1. Department of Languages and Computer Systems, University of Seville, Reina Mercedes Avenue, Seville, 41012, Spain

    Antonio J. Tallón-Ballesteros & José C. Riquelme

Authors
  1. Antonio J. Tallón-Ballesteros
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  2. José C. Riquelme
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Corresponding author

Correspondence to Antonio J. Tallón-Ballesteros .

Editor information

Editors and Affiliations

  1. Department of Software Engineering Faculty of Information and, Technical University of Malaysia Melaka (UTeM), Durian Tunggal, Malaysia

    Azah Kamilah Muda

  2. Department of Software Engineering Faculty of Info. and Comm. Tech., Technical University of Malaysia Melaka (UTeM), Durian Tunggal, Malaysia

    Yun-Huoy Choo

  3. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, Washington, USA

    Ajith Abraham

  4. Dept. of Computer Sci. and Engineering Center of Excellence for Document, The State University of New York SUNY, Buffalo, New York, USA

    Sargur N. Srihari

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Tallón-Ballesteros, A.J., Riquelme, J.C. (2014). Data Mining Methods Applied to a Digital Forensics Task for Supervised Machine Learning. In: Muda, A., Choo, YH., Abraham, A., N. Srihari, S. (eds) Computational Intelligence in Digital Forensics: Forensic Investigation and Applications. Studies in Computational Intelligence, vol 555. Springer, Cham. https://doi.org/10.1007/978-3-319-05885-6_17

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  • DOI: https://doi.org/10.1007/978-3-319-05885-6_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05884-9

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