Saved in:
| Main Authors: | , |
|---|---|
| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
|
| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.20080544 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866901519521021952 |
|---|---|
| author | Safa Mohamed Safa Mostafa |
| author_facet | Safa Mohamed Safa Mostafa |
| contents | <p class="MsoNormal">This article presents a comprehensive examination of iot device security challenges and solutions, 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 firmware vulnerabilities, default credentials, OTA update security, and device authentication. 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_20080544 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | IoT Device Security Challenges and Solutions Safa Mohamed Safa Mostafa Machine learning <p class="MsoNormal">This article presents a comprehensive examination of iot device security challenges and solutions, 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 firmware vulnerabilities, default credentials, OTA update security, and device authentication. 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 | IoT Device Security Challenges and Solutions |
| topic | Machine learning |
| url | https://doi.org/10.5281/zenodo.20080544 |