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Generative Methods for Out-of-distribution Prediction and Applications for Threat Detection and Analysis: A Short Review

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Digital Sovereignty in Cyber Security: New Challenges in Future Vision (CyberSec4Europe 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1807))

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Abstract

In recent times, Machine Learning has played an important role in developing novel advanced tools for threat detection and mitigation. Intrusion Detection, Misinformation, Malware, and Fraud Detection are just some examples of cybersecurity fields in which Machine Learning techniques are used to reveal the presence of malicious behaviors. However, Out-of-Distribution, i.e., the potential distribution gap between training and test set, can heavily affect the performances of the traditional Machine Learning based methods. Indeed, they could fail in identifying out-of-samples as possible threats, therefore devising robust approaches to cope with this issue is a crucial and relevant challenge to mitigate the risk of undetected attacks. Moreover, a recent emerging line proposes to use generative models to yield synthetic likely examples to feed the learning algorithms. In this work, we first survey recent Machine Learning and Deep Learning based solutions to face both the problems, i.e., outlier detection and generation; then we illustrate the main cybersecurity application scenarios in which these approaches have been adopted successfully.

E. Coppolillo, A. Liguori, M. Guarascio, and F. Sergio Pisani—Equally contributed to the paper and are all considered first authors.

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Acknowledgements

This work was partially supported by EU H2020-SU-ICT-03-2018 Project No. 830929 CyberSec4Europe (cybersec4europe.eu) and by project SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU.

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Calabria, Rende, Italy

    Erica Coppolillo

  2. Department of Computer Engineering, Modeling, Electronics and Systems, University of Calabria, Rende, Italy

    Angelica Liguori

  3. Institute for High Performance Computing and Networking of Italian National Research Council, Rende, Italy

    Erica Coppolillo, Angelica Liguori, Massimo Guarascio, Francesco Sergio Pisani & Giuseppe Manco

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  1. Erica Coppolillo
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  2. Angelica Liguori
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  3. Massimo Guarascio
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  4. Francesco Sergio Pisani
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  5. Giuseppe Manco
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Corresponding author

Correspondence to Angelica Liguori .

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Editors and Affiliations

  1. University of Murcia, Murcia, Spain

    Antonio Skarmeta

  2. Politecnico di Torino, Turin, Italy

    Daniele Canavese

  3. Politecnico di Torino, Turin, Italy

    Antonio Lioy

  4. University of Murcia, Murcia, Spain

    Sara Matheu

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Coppolillo, E., Liguori, A., Guarascio, M., Pisani, F.S., Manco, G. (2023). Generative Methods for Out-of-distribution Prediction and Applications for Threat Detection and Analysis: A Short Review. In: Skarmeta, A., Canavese, D., Lioy, A., Matheu, S. (eds) Digital Sovereignty in Cyber Security: New Challenges in Future Vision. CyberSec4Europe 2022. Communications in Computer and Information Science, vol 1807. Springer, Cham. https://doi.org/10.1007/978-3-031-36096-1_5

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  • DOI: https://doi.org/10.1007/978-3-031-36096-1_5

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