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| Main Authors: | , , |
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| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.20065545 |
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| _version_ | 1866901570779611136 |
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| author | Safa mohamed Safa Kamal Safa Mostafa |
| author_facet | Safa mohamed Safa Kamal Safa Mostafa |
| contents | <p class="MsoNormal">This article presents a comprehensive examination of securing industrial control systems against cyber threats, addressing the critical challenges and opportunities at the intersection of cybersecurity, advanced system architecture, and artificial intelligence. The study synthesizes insights from 21 peer-reviewed references spanning digital twin security, adaptive defense frameworks, deep learning-based anomaly detection, cloud-IoT security management, encrypted search optimization, 5G network security, massive MIMO signal processing, privacy-preserving architectures, and generative model applications. Each reference is individually cited and contextualized within the broader discourse on SCADA security, PLC vulnerabilities, air-gapped network protection, and supply chain risks. The article examines how these diverse research contributions collectively inform the design, implementation, and evaluation of robust solutions for contemporary security and architectural challenges. By integrating technical analyses with organizational and practical considerations, this work provides a holistic perspective that is relevant to both researchers and practitioners working to advance the state of the art in cybersecurity.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20065545 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Securing Industrial Control Systems Against Cyber Threats Safa mohamed Safa Kamal Safa Mostafa Machine Learning <p class="MsoNormal">This article presents a comprehensive examination of securing industrial control systems against cyber threats, addressing the critical challenges and opportunities at the intersection of cybersecurity, advanced system architecture, and artificial intelligence. The study synthesizes insights from 21 peer-reviewed references spanning digital twin security, adaptive defense frameworks, deep learning-based anomaly detection, cloud-IoT security management, encrypted search optimization, 5G network security, massive MIMO signal processing, privacy-preserving architectures, and generative model applications. Each reference is individually cited and contextualized within the broader discourse on SCADA security, PLC vulnerabilities, air-gapped network protection, and supply chain risks. The article examines how these diverse research contributions collectively inform the design, implementation, and evaluation of robust solutions for contemporary security and architectural challenges. By integrating technical analyses with organizational and practical considerations, this work provides a holistic perspective that is relevant to both researchers and practitioners working to advance the state of the art in cybersecurity.</p> |
| title | Securing Industrial Control Systems Against Cyber Threats |
| topic | Machine Learning |
| url | https://doi.org/10.5281/zenodo.20065545 |