Outputs (16)

Hardware security modules for secure communications in the industrial Internet of Things (2025)
Journal Article

The Industrial Internet of Things (IIoT) offers transformative potential but introduces critical security risks, including unauthorized access, data breaches, and privacy compromise. Hardware Security Modules (HSMs) have emerged as robust solutions t... Read More about Hardware security modules for secure communications in the industrial Internet of Things.

Federated learning meets recursive self-distillation: A scalable malware detection framework for IoVs (2025)
Presentation / Conference Contribution

This paper proposes an integrated approach called FL-RSD, leveraging the key advantages of Federated Learning (FL) and Recursive Self-Distillation (RSD) for malware detection in the Internet of Vehicles (IoV). The proposed FL-RSD framework enhances m... Read More about Federated learning meets recursive self-distillation: A scalable malware detection framework for IoVs.

ARUC-GAN: A CycleGAN-based attention residual U-Net for low-dose CT denoising in smart healthcare (2025)
Journal Article

Low-dose computed tomography (LDCT) is increasingly adopted in medical imaging to minimize radiation exposure. However, the diagnostic accuracy is primarily affected by different noise sources, e.g., quantum, electronic, and reconstruction. Moreover,... Read More about ARUC-GAN: A CycleGAN-based attention residual U-Net for low-dose CT denoising in smart healthcare.

Federated learning in IoT environments: Examining the three-way see-saw for privacy, model-performance, and network-efficiency (2025)
Journal Article

This survey paper provides an in-depth exploration of Federated Learning (FL) in Internet of Things (IoT) environments , focusing on privacy-preserving techniques and their influence on model performance and network efficiency. It highlights key chal... Read More about Federated learning in IoT environments: Examining the three-way see-saw for privacy, model-performance, and network-efficiency.

An integrated approach to mitigate poisoning attacks in federated learning frameworks (2025)
Presentation / Conference Contribution

While Federated learning (FL) is considered privacy-preserving by nature, it remains vulnerable to many attacks, such as data and model poisoning, that compromise data integrity and model accuracy. Conventional privacy-preserving federated learning (... Read More about An integrated approach to mitigate poisoning attacks in federated learning frameworks.

Privacy-enhanced sentiment analysis in mental health: Federated learning with data obfuscation and bidirectional encoder representations from transformers (2024)
Journal Article

This research aims to find an optimal balance between privacy and performance in forecasting mental health sentiment. This paper investigates federated learning (FL) augmented with a novel data obfuscation (DO) technique, where synthetic data is used... Read More about Privacy-enhanced sentiment analysis in mental health: Federated learning with data obfuscation and bidirectional encoder representations from transformers.

A novel hybrid framework for realistic UAV detection using a mixed RF signal database (2024)
Presentation / Conference Contribution

Advances in Unmanned Aerial Vehicles (UAVs) empower a plethora of applications but also raise significant security and privacy challenges. Effective UAVs detection systems are crucial for mitigating these risks. This paper deals with this problem and... Read More about A novel hybrid framework for realistic UAV detection using a mixed RF signal database.

A lightweight integrity-driven federated learning approach to mitigate poisoning attacks in IoT (2024)
Presentation / Conference Contribution

Despite its distributed nature and being privacy-preserving by nature, Federated Learning (FL) is vulnerable to poisoning attacks in which malicious actors can inject fake model parameters or false data to compromise the learning process. This articl... Read More about A lightweight integrity-driven federated learning approach to mitigate poisoning attacks in IoT.

A novel hybrid framework for realistic UAV detection using a mixed RF signal database (2024)
Presentation / Conference Contribution

Advances in Unmanned Aerial Vehicles (UAVs) empower a plethora of applications but also raise significant security and privacy challenges. Effective UAVs detection systems are crucial for mitigating these risks. This paper deals with this problem and... Read More about A novel hybrid framework for realistic UAV detection using a mixed RF signal database.

A survey on emerging blockchain technology platforms for securing the Internet of Things (2024)
Journal Article

The adoption of blockchain platforms to bolster the security of Internet of Things (IoT) systems has attracted significant attention in recent years. Currently, there is a lack of comprehensive and systematic survey papers in the literature addressin... Read More about A survey on emerging blockchain technology platforms for securing the Internet of Things.