<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Due to the increasing sophistication and frequency of cyber threats, cyber security has become a pressing issue in today’s interconnected society. The traditional security methods have proven insufficient to tackle the evolving threat landscape, leading to the need for more advanced and effective defence mechanisms. The emerging field of deep neural learning has shown promise in cybersecurity, as it can learn complex patterns and behaviors in data and provide more accurate and efficient detection and prediction capabilities. This research aims to develop a robust cybersecurity defense system, named NeuroCyberGuard, by using deep neural learning-based mathematical modelling. The proposed approach leverages the power of deep neural networks to analyze large amounts of network traffic data, identify potential threats, and provide real-time alerts and responses to prevent security breaches. The mathematical modelling component enables the system to optimize its performance and adapt to changing network conditions and attack patterns. The system will be evaluated using a variety of benchmark datasets and real-world scenarios to validate its effectiveness and efficiency. The expected outcome of this research is a state-of-the-art cybersecurity defense system that provides comprehensive protection against cyber threats and enhances the security posture of organizations.</p>
</abstract>

Due to the increasing sophistication and frequency of cyber threats, cyber security has become a pressing issue in today’s interconnected society. The traditional security methods have proven insufficient to tackle the evolving threat landscape, leading to the need for more advanced and effective defence mechanisms. The emerging field of deep neural learning has shown promise in cybersecurity, as it can learn complex patterns and behaviors in data and provide more accurate and efficient detection and prediction capabilities. This research aims to develop a robust cybersecurity defense system, named NeuroCyberGuard, by using deep neural learning-based mathematical modelling. The proposed approach leverages the power of deep neural networks to analyze large amounts of network traffic data, identify potential threats, and provide real-time alerts and responses to prevent security breaches. The mathematical modelling component enables the system to optimize its performance and adapt to changing network conditions and attack patterns. The system will be evaluated using a variety of benchmark datasets and real-world scenarios to validate its effectiveness and efficiency. The expected outcome of this research is a state-of-the-art cybersecurity defense system that provides comprehensive protection against cyber threats and enhances the security posture of organizations.

NeuroCyberGuard: Developing a Robust Cybersecurity Defense System through Deep Neural Learning-Based Mathematical Modeling

Information Technology Department, Engineering College

Journal of Smart Internet of Things

This work is licensed under the Creative Commons Attribution 4.0 International License.

Due to the increasing sophistication and frequency of cyber threats, cyber security has become a pressing issue in today’s interconnected society. The...

NeuroCyberGuard: Developing a Robust Cybersecurity Defense System through Deep Neural Learning-Based Mathematical Modeling

Online veröffentlicht: 15. Dez. 2022

Seitenbereich: 133 - 145

Eingereicht: 20. Aug. 2023

Akzeptiert: 21. Okt. 2023

DOI: https://doi.org/10.2478/jsiot-2022-0009

Schlüsselwörter
Cybersecurity, Deep Neural Learning, Mathematical Modeling, Network Traffic Analysis, Threat Detection

© 2022 Ghazi Ghader Mohammed et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

NeuroCyberGuard: Developing a Robust Cybersecurity Defense System through Deep Neural Learning-Based Mathematical Modeling

Online veröffentlicht: 15. Dez. 2022

Seitenbereich: 133 - 145

Eingereicht: 20. Aug. 2023

Akzeptiert: 21. Okt. 2023

DOI: https://doi.org/10.2478/jsiot-2022-0009

SchlüsselwörterCybersecurity, Deep Neural Learning, Mathematical Modeling, Network Traffic Analysis, Threat Detection

© 2022 Ghazi Ghader Mohammed et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Cybersecurity, Deep Neural Learning, Mathematical Modeling, Network Traffic Analysis, Threat Detection