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Cybersecurity Text Data Classification and Optimization for CTI Systems

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Web, Artificial Intelligence and Network Applications (WAINA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1150))

Abstract

Cyber threat intelligence systems provide a way to prioritize alerts and allow security teams to focus on critical threats and utilize their resources more efficiently. One challenge in these systems comes in accurately classifying the data that is input and processed within the system which is critical to producing meaningful output. To tackle this problem, in this paper we research text-based cybersecurity data classification methods using a multi-layer keyword filtering method and unsupervised learning methods using doc2vec. We also look at how we can optimize the accuracy and efficiency of cyber threat intelligence systems through the use of ensemble learning. This research will help with prioritization of cyber threat intelligence systems which allow security teams to use their resources more efficiently.

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Acknowledgements

This research was supported by JSPS KAKENHI Grant Number JP16K00480.

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Authors and Affiliations

  1. Kyushu University, Fukuoka, Japan

    Ariel Rodriguez & Koji Okamura

Authors
  1. Ariel Rodriguez
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  2. Koji Okamura
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Corresponding author

Correspondence to Koji Okamura .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Electrical Engineering and Information Technology, University of Naples “Frederico II”, Naples, Italy

    Flora Amato

  3. Department of Political Science, University of Campania Luigi Vanvitelli, Caserta, Italy

    Francesco Moscato

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  5. Department of Advanced Sciences, Hosei University, Tokyo, Japan

    Makoto Takizawa

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Rodriguez, A., Okamura, K. (2020). Cybersecurity Text Data Classification and Optimization for CTI Systems. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_37

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