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Volume 10
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Proceeding Paper

Enhancing Security and Privacy in IoT Data Streams: Real-Time Anomaly Detection for Threat Mitigation in Traffic Management †

by
Oumayma Berraadi
1,
Hicham Gibet Tani
2 and
Mohamed Ben Ahmed
1
1
Department of Computer Science, FSTT, Abdelmalek Essaadi University, Tetouan 93000, Morocco
2
Department of Computer Science, FPL, Abdelmalek Essaadi University, Tetouan 93000, Morocco
†
Presented at the International Conference on Sustainable Computing and Green Technologies (SCGT’2025), Larache, Morocco, 14–15 May 2025.
Comput. Sci. Math. Forum 2025, 10(1), 8; https://doi.org/10.3390/cmsf2025010008
Published: 16 June 2025
(This article belongs to the Proceedings of International Conference on Sustainable Computing and Green Technologies (SCGT’2025))
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Abstract

The rapid expansion of IoT in smart cities has improved traffic management but increased security risks. Traditional IDS struggle with advanced threats, prompting adaptive solutions. This work proposes a framework combining machine learning (ML), Zero Trust Architecture (ZTA), and blockchain authentication. Supervised models (XGBoost, RF, SVM, LR) detect known anomalies, while a CNN Autoencoder identifies novel threats. Blockchain ensures identity integrity, and compromised devices are isolated automatically. Tests on the IoT-23 dataset demonstrate superior accuracy, fewer false positives, and better scalability than conventional methods. The integration of AI, Zero Trust, and blockchain significantly boosts IoT traffic system security and resilience.
Keywords: IoT data streams; threat mitigation; real-time anomaly detection; IoT security; machine learning; traffic management; smart city security; big data analytics; blockchain authentication IoT data streams; threat mitigation; real-time anomaly detection; IoT security; machine learning; traffic management; smart city security; big data analytics; blockchain authentication

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MDPI and ACS Style

Berraadi, O.; Gibet Tani, H.; Ben Ahmed, M. Enhancing Security and Privacy in IoT Data Streams: Real-Time Anomaly Detection for Threat Mitigation in Traffic Management. Comput. Sci. Math. Forum 2025, 10, 8. https://doi.org/10.3390/cmsf2025010008

AMA Style

Berraadi O, Gibet Tani H, Ben Ahmed M. Enhancing Security and Privacy in IoT Data Streams: Real-Time Anomaly Detection for Threat Mitigation in Traffic Management. Computer Sciences & Mathematics Forum. 2025; 10(1):8. https://doi.org/10.3390/cmsf2025010008

Chicago/Turabian Style

Berraadi, Oumayma, Hicham Gibet Tani, and Mohamed Ben Ahmed. 2025. "Enhancing Security and Privacy in IoT Data Streams: Real-Time Anomaly Detection for Threat Mitigation in Traffic Management" Computer Sciences & Mathematics Forum 10, no. 1: 8. https://doi.org/10.3390/cmsf2025010008

APA Style

Berraadi, O., Gibet Tani, H., & Ben Ahmed, M. (2025). Enhancing Security and Privacy in IoT Data Streams: Real-Time Anomaly Detection for Threat Mitigation in Traffic Management. Computer Sciences & Mathematics Forum, 10(1), 8. https://doi.org/10.3390/cmsf2025010008

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