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Classification of Cybercrime Indicators in Open Social Data

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Information Management and Big Data (SIMBig 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1410))

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Abstract

Posting information on social media platforms is a popular activity through which personal and confidential information can leak into the public domain. Consequently, social media can contain information that provides an indication that an organization has been compromised or suffered a data breach. This paper describes a technique for inferring if an organization has been compromised from information posted on social media. The proposed strategy forms the basis of an alarm system which generates an alert for possible unreported cybercrime incidents. The proposed strategy used two social media cybercrime related datasets that were collected from the Irish and New York regions from financial organizations’ Twitter accounts. The Tweets are labelled as either containing cybercrime indicators or not, and then the cybercrime Tweets were labelled further into crime categories. A deep dense pyramidal Neural Network model is used to classify the Tweets. This approach achieves an AUC of \(~0.85 \pm 0.03\) which outperforms the baseline of deep convolutional neural networks.

This project is funded by IBM and Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.

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Author information

Authors and Affiliations

  1. Computer Science, National University of Ireland Galway, Galway, Ireland

    Ihsan Ullah, Brett Drury & Michael G. Madden

  2. Insight Center for Data Analytics, National University of Ireland Galway, Galway, Ireland

    Ihsan Ullah, Caoilfhionn Lane, Marc Mellotte & Michael G. Madden

  3. Cognitive Computing Group, Innovation Exchange, IBM, Dublin, Ireland

    Teodora Sandra Buda & Haytham Assem

Authors
  1. Ihsan Ullah
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  2. Caoilfhionn Lane
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  3. Teodora Sandra Buda
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  4. Brett Drury
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  5. Marc Mellotte
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  6. Haytham Assem
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  7. Michael G. Madden
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Corresponding author

Correspondence to Michael G. Madden .

Editor information

Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Juan Antonio Lossio-Ventura

  2. Visibilia, São Paulo, Brazil

    Jorge Carlos Valverde-Rebaza

  3. University of Valencia, Valencia, Spain

    Eduardo Díaz

  4. Universidad del Pacífico, Lima, Peru

    Hugo Alatrista-Salas

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Ullah, I. et al. (2021). Classification of Cybercrime Indicators in Open Social Data. In: Lossio-Ventura, J.A., Valverde-Rebaza, J.C., Díaz, E., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2020. Communications in Computer and Information Science, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-76228-5_23

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  • DOI: https://doi.org/10.1007/978-3-030-76228-5_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-76227-8

  • Online ISBN: 978-3-030-76228-5

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