Abstract
In the last decades digital forensics have become a prominent activity in modern investigations. Indeed, an important data source is often constituted by information contained in devices on which investigational activity is performed. Due to the complexity of this inquiring activity, the digital tools used for investigation constitute a central concern. In this paper a clustering-based text mining technique is introduced for investigational purposes. The proposed methodology is experimentally applied to the publicly available Enron dataset that well fits a plausible forensics analysis context.
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Decherchi, S., Tacconi, S., Redi, J., Leoncini, A., Sangiacomo, F., Zunino, R. (2009). Text Clustering for Digital Forensics Analysis. In: Herrero, Á., Gastaldo, P., Zunino, R., Corchado, E. (eds) Computational Intelligence in Security for Information Systems. Advances in Intelligent and Soft Computing, vol 63. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04091-7_4
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DOI: https://doi.org/10.1007/978-3-642-04091-7_4
Publisher Name: Springer, Berlin, Heidelberg
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