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| Main Authors: | , , , |
|---|---|
| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19552974 |
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Table of Contents:
- <p class="MsoNormal"><span>The fact that cyber attacks are getting more complicated shows that perimeter-based security models don't do a good job of protecting complicated systems. This paper talks about a next-generation firewall (NGFW) that uses AI to help find new threats and make Zero Trust Architectures (ZTA) work better. The proposed solution uses advanced AI and machine learning to go beyond the usual static rule-based filtering of network traffic and user behavior. It analyzes each instance in real time.</span></p> <p class="MsoNormal"><span>The main goal of the study is to create a smart security framework based on the principle of "never trust, always verify." This is achieved by building deep learning models to detect anomalies and potential intrusions, which improves accuracy and lowers false positives. The thesis also looks into using automated logging on the blockchain for unchangeable auditing and the application of automated threat response systems. The study shows that an AI-powered approach greatly boosts an organization's security stance, offering a scalable and flexible solution to current cybersecurity challenges faced by enterprises.</span></p>