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Secure and Resilient Solutions for CCTV, Small Sensor and IoT Device Security

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 25 December 2025 | Viewed by 1277

Special Issue Editor

Special Issue Information

Dear Colleagues,

The rapid proliferation of IoT devices, including small sensors and CCTV systems, has revolutionized industries such as surveillance, healthcare, smart cities, and critical infrastructure monitoring. However, the pervasive connectivity and heterogeneity of these systems expose them to significant cybersecurity challenges, including data tampering, unauthorized access, and advanced persistent threats. This Special Issue invites cutting-edge research and innovative approaches to securing IoT ecosystems with a focus on CCTV systems and small sensors.

Key themes include designing lightweight cryptographic protocols, anomaly detection techniques, and federated learning models tailored for resource-constrained IoT devices. Submissions exploring hardware-based security enhancements, blockchain-enabled authentication, and quantum-resistant algorithms for IoT security are also encouraged.

By fostering advancements in secure IoT frameworks, this Special Issue aims to address vulnerabilities, promote data integrity, and ensure the reliability of IoT-based applications, aligning closely with the scope of Sensors. Topics may range from theoretical foundations to practical implementations, bridging gaps between sensor technology, IoT networks, and cybersecurity.

Prof. Dr. Jong Hyuk Park
Guest Editor

Manuscript Submission Information

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Keywords

  • IoT security
  • CCTV anomaly detection
  • lightweight cryptography
  • blockchain for IoT
  • secure IoT frameworks
  • sensor authentication
  • cyber threat mitigation
  • federated learning for IoT
  • quantum-resistant algorithms
  • resilient IoT architectures

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Published Papers (1 paper)

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Research

27 pages, 2104 KiB  
Article
FL-TENB4: A Federated-Learning-Enhanced Tiny EfficientNetB4-Lite Approach for Deepfake Detection in CCTV Environments
by Jimin Ha, Abir El Azzaoui and Jong Hyuk Park
Sensors 2025, 25(3), 788; https://doi.org/10.3390/s25030788 - 28 Jan 2025
Viewed by 1125
Abstract
The widespread deployment of CCTV systems has significantly enhanced surveillance and public safety across various environments. However, the emergence of deepfake technology poses serious challenges by enabling malicious manipulation of video footage, compromising the reliability of CCTV systems for evidence collection and privacy [...] Read more.
The widespread deployment of CCTV systems has significantly enhanced surveillance and public safety across various environments. However, the emergence of deepfake technology poses serious challenges by enabling malicious manipulation of video footage, compromising the reliability of CCTV systems for evidence collection and privacy protection. Existing deepfake detection solutions often suffer from high computational overhead and are unsuitable for real-time deployment on resource-constrained CCTV cameras. This paper proposes FL-TENB4, a Federated-Learning-enhanced Tiny EfficientNetB4-Lite framework for deepfake detection in CCTV environments. The proposed architecture integrates Tiny Machine Learning (TinyML) techniques with EfficientNetB4-Lite, a lightweight convolutional neural network optimized for edge devices, and employs a Federated Learning (FL) approach for collaborative model updates. The TinyML-based local model ensures real-time deepfake detection with minimal latency, while FL enables privacy-preserving training by aggregating model updates without transferring sensitive video data to centralized servers. The effectiveness of the proposed system is validated using the FaceForensics++ dataset under resource-constrained conditions. Experimental results demonstrate that FL-TENB4 achieves high detection accuracy, reduced model size, and low inference latency, making it highly suitable for real-world CCTV environments. Full article
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