Deep Learning Applications on Cybersecurity

  1. Carlos Lago 1
  2. Rafael Romón 1
  3. Iker Pastor López 1
  4. Borja Sanz Urquijo 1
  5. Alberto Tellaeche 1
  6. Pablo García Bringas 1
  1. 1 Universidad de Deusto
    info

    Universidad de Deusto

    Bilbao, España

    ROR https://ror.org/00ne6sr39

Libro:
Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings
  1. Hugo Sanjurjo González (coord.)
  2. Iker Pastor López (coord.)
  3. Pablo García Bringas (coord.)
  4. Héctor Quintián (coord.)
  5. Emilio Corchado (coord.)

Editorial: Springer International Publishing AG

ISBN: 978-3-030-86271-8,  978-3-030-86270-1

Año de publicación: 2021

Páginas: 611-621

Congreso: Hybrid Artificial Intelligent Systems (HAIS) (16. 2021. Bilbao)

Tipo: Aportación congreso

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Resumen

Security has always been one of the biggest challenges faced by computer systems, recent developments in the field of Machine Learning are affecting almost all aspects of computer science and Cybersecurity is no different. In this paper, we have focused on studying the possible application of deep learning techniques to three different problems faced by Cybersecurity: SPAM filtering, malware detection and adult content detection in order to showcase the benefits of applying them. We have tested a wide variety of techniques, we have applied LSTMs for spam filtering, then, we have used DNNs for malware detection and finally, CNNs in combination with Transfer Learning for adult content detection, as well as applying image augmentation techniques to improve our dataset. We have managed to reach an AUC over 0.9 on all cases, demonstrating that it is possible to build cost-effective solutions with excellent performance without complex architectures.

Fuente de los datos: Dialnet