All Outputs (4)

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.

A novel hybrid framework for realistic UAV detection using a mixed RF signal database
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
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
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.