Outputs (9)

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.

Explainable AI in medical imaging: An interpretable and collaborative federated learning model for brain tumor classification (2025)
Journal Article

Introduction: A brain tumor is a collection of abnormal cells in the brain that can become life-threatening due to its ability to spread. Therefore, a prompt and meticulous classification of the brain tumor is an essential element in healthcare care.... Read More about Explainable AI in medical imaging: An interpretable and collaborative federated learning model for brain tumor classification.

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.

Intrusion detection framework for the Internet of Things using a dense random neural network (2021)
Journal Article

The Internet of Things (IoT) devices, networks, and applications have become an integral part of modern societies. Despite their social, economic, and industrial benefits, these devices and networks are frequently targeted by cybercriminals. Hence, I... Read More about Intrusion detection framework for the Internet of Things using a dense random neural network.