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Securing the Internet of Things: Challenges and Advances in Cybersecurity

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1177

Special Issue Editors


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Guest Editor
School of Computing and Digital Technology, Birmingham City University, Birmingham, UK
Interests: machine learning; smart sensing; environment monitoring; video processing; Internet of Things; big data

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Guest Editor
School of Computing, Southern Illinois University Carbondale, Carbondale, IL 62901, USA
Interests: parallel computing systems; distributed computing systems; mobile cloud computing systems; mobile edge computing systems; Internet of Things

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Guest Editor
Computer Science and Creative Technologies, University of the West of England, Bristol 133798, UK
Interests: machine learning; healthcare; IoT; cybersecurity AI
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Special Issue Information

Dear Colleagues,

From wearable devices and smart thermostats to industrial machinery and autonomous vehicles, the Internet of Things (IoT), embedding connectivity into daily objects, has radically transformed the way we interact with the world, As billions of devices are now available online, the promise of automation, efficiency, and data-driven insights grow exponentially; however, this is not without potential risk. IoT systems are inherently complex and often lack standard security protocols, making them attractive targets for cyberattacks. Devices with limited processing power may not support robust encryption, and their widespread deployment across homes, cities, and industries makes them vulnerable. From Botnet-driven DDoS attacks to data breaches and remote hijacking, such vulnerabilities are real and evolving. As a result, cybersecurity for IoT devices has become a rapidly advancing field. Researchers and developers are exploring edge computing, AI-driven threat detection, blockchain-based authentication, and zero-trust architectures to fortify these networks. Regulatory frameworks such as GDPR and NIS2 recommend stronger accountability and data protection.

This Special Issue welcomes original research and review articles that address the multifaceted challenges of securing IoT systems, highlighting the latest technological and strategic advances aimed at safeguarding this hyper-connected world.

Dr. Ambreen Hussain
Dr. Sayed Chhattan Shah
Dr. Qurat-Ul-Ain Mastoi
Guest Editors

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Keywords

  • IoT security protocols
  • intrusion detection
  • edge computing security
  • thread mitigation
  • IoT security challenges
  • secure communication
  • threat mitigation
  • data privacy
  • sensor networks security

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Published Papers (1 paper)

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Research

24 pages, 3950 KB  
Article
Temporal Tampering Detection in Automotive Dashcam Videos via Multi-Feature Forensic Analysis and a 1D Convolutional Neural Network
by Ali Rehman Shinwari, Uswah Binti Khairuddin and Mohamad Fadzli Bin Haniff
Sensors 2026, 26(2), 517; https://doi.org/10.3390/s26020517 - 13 Jan 2026
Viewed by 731
Abstract
Automotive dashboard cameras are widely used to record driving events and often serve as critical evidence in accident investigations and insurance claims. However, the availability of free and low-cost editing tools has increased the risk of video tampering, underscoring the need for reliable [...] Read more.
Automotive dashboard cameras are widely used to record driving events and often serve as critical evidence in accident investigations and insurance claims. However, the availability of free and low-cost editing tools has increased the risk of video tampering, underscoring the need for reliable methods to verify video authenticity. Temporal tampering typically involves manipulating frame order through insertion, deletion, or duplication. This paper proposes a computationally efficient framework that transforms high-dimensional video into compact one-dimensional temporal signals and learns tampering patterns using a shallow one-dimensional convolutional neural network (1D-CNN). Five complementary features are extracted between consecutive frames: frame-difference magnitude, structural similarity drift (SSIM drift), optical-flow mean, forward–backward optical-flow consistency error, and compression-aware temporal prediction error. Per-video robust normalization is applied to emphasize intra-video anomalies. Experiments on a custom dataset derived from D2-City demonstrate strong detection performance in single-attack settings: 95.0% accuracy for frame deletion, 100.0% for frame insertion, and 95.0% for frame duplication. In a four-class setting (non-tampered, insertion, deletion, duplication), the model achieves 96.3% accuracy, with AUCs of 0.994, 1.000, 0.997, and 0.988, respectively. Efficiency analysis confirms near real-time CPU inference (≈12.7–12.9 FPS) with minimal memory overhead. Cross-dataset tests on BDDA and VIRAT reveal domain-shift sensitivity, particularly for deletion and duplication, highlighting the need for domain adaptation and augmentation. Overall, the proposed multi-feature 1D-CNN provides a practical, interpretable, and resource-aware solution for temporal tampering detection in dashcam videos, supporting trustworthy video forensics in IoT-enabled transportation systems. Full article
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