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An Autonomous Self-learning and Self-adversarial Training Neural Architecture for Intelligent and Resilient Cyber Security Systems

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Engineering Applications of Neural Networks (EANN 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1826))

Abstract

Cybersecurity systems have become increasingly important as businesses and individuals rely more on technology. However, the increasing complexity of these systems and the evolving nature of cyber threats require innovative solutions to protect against cyber attacks. One promising approach is the idea of autonomous self-learning and auto-training neural architectures. Autonomous self-learning refers to the ability of the system to adapt to new threats and learn from past experiences without human intervention. Auto-training, on the other hand, refers to the ability of the system to improve its performance over time by automatically adjusting its parameters and algorithms. This research proposes an autonomous Self-Learning and Self-Adversarial Training (SLSAT) neural architecture for intelligent and resilient cyber security systems. It is an extension of the next-generation Continuous-Time Reservoir Computing (CTRC) that was proposed by the authors recently. The CTRC is a time-series anomaly detection system controlled by time-varying differential equations. It uses Reinforcement Learning (RL) to dynamically fine-tune the reservoir computing parameters in order to identify the aberrant changes in the data. The proposed method in this research improves the CTRC’s architecture by including a Conditional Tabular Generative Adversarial Network (CTGAN). Specifically, including CTGAN allows the SLSAT architecture to generate synthetic data based on the identified abnormalities to improve the model’s performance and adapt to new and evolving threats without manual intervention. This, as proved experimentally, helps the model identify aberrant changes in the data and fend off poison and zero-day attacks.

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Author information

Authors and Affiliations

  1. School of Science and Technology, Informatics Studies, Hellenic Open University, Patras, Greece

    Konstantinos Demertzis

  2. Department of Civil Engineering, Faculty of Mathematics Programming and General Courses, School of Engineering, Democritus University of Thrace, Kimmeria, Xanthi, Greece

    Lazaros Iliadis

Authors
  1. Konstantinos Demertzis
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  2. Lazaros Iliadis
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Corresponding author

Correspondence to Konstantinos Demertzis .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  3. University of Leon, León, Spain

    Serafin Alonso

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

  5. University of the West of England, Bristol, UK

    Elias Pimenidis

Appendix 1

Appendix 1

Fig. 3.
figure 9

The autonomous self-learning and self-adversarial training neural architecture

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Demertzis, K., Iliadis, L. (2023). An Autonomous Self-learning and Self-adversarial Training Neural Architecture for Intelligent and Resilient Cyber Security Systems. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_38

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  • DOI: https://doi.org/10.1007/978-3-031-34204-2_38

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  • Publisher Name: Springer, Cham

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