As wireless networks evolve toward cell-free massive MIMO architectures, ensuring secure and reliable communication becomes increasingly challenging. One critical threat is jamming attacks, which can disrupt communication in complex and dynamic network environments. This paper proposes a novel jamming detection… Read More »Jamming Detection in Cell-Free MIMO with Dynamic Graphs
Physical layer security (PLS) offers a promising alternative to traditional cryptographic methods, particularly for resource-constrained devices such as IoT sensors. However, secret key generation (SKG) techniques typically rely on channel variations caused by mobility—making them less effective in low-mobility or… Read More »Enhancing Secret Key Generation in Low-Mobility Scenarios by Locally Generated Pilots
Federated learning (FL) has emerged as a key approach for training machine learning models without sharing raw data, making it highly relevant for privacy-sensitive applications. However, many existing FL frameworks rely on a central coordinator, which can introduce bottlenecks and… Read More »DEMO: NEBULA – Decentralized Federated Learning for Heterogeneous Networks
Secure communication is expected to become even more critical in future 6G networks, particularly as emerging technologies such as quantum computing threaten traditional public-key encryption schemes. To explore alternative security approaches, researchers have developed a context-aware secret key generation (SKG)… Read More »Context-Aware Secret Key Generation Demonstrator based on Physical Layer Security
This paper examines how federated learning (FL) can be made more reliable and efficient in vehicular environments, where vehicles act as learning clients while continuously moving across the road network. Mobility creates challenges such as fluctuating wireless connectivity, variable computation… Read More »VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning
Software-Defined Networking (SDN) offers flexibility and centralized control, but this same architecture makes SDN controllers highly vulnerable to Distributed Denial of Service (DDoS) attacks. When attackers flood the controller with Packet-In messages—often using spoofed IP addresses—they can degrade performance or… Read More »Image-Based Frequency-Domain Analysis for Robust DDoS Detection in SDN
As drones become increasingly embedded in civilian systems—from environmental monitoring to disaster relief—the security of their wireless links has emerged as a critical concern. Spoofing and jamming attacks can interfere with navigation or inject false messages, making robust authentication mechanisms… Read More »Challenge-Response to Authenticate Drone Communications: A Game Theoretic Approach
Training spiking neural networks (SNNs) remains a longstanding challenge due to their non-differentiable spike activation functions. Most existing approaches adapt methods designed for traditional artificial neural networks, such as backpropagation with surrogate gradients. While widely used, these surrogate techniques introduce… Read More »ADMM-Based Training for Spiking Neural Networks
This paper investigates how effectively Distributed Denial-of-Service (DDoS) attacks can be detected in two different network architectures—traditional networks and Software-Defined Networking (SDN)—by using both time-domain and frequency-domain features. While earlier research has mostly relied on statistical or time-based characteristics of… Read More »A Comparative Study of DDoS Attack Detection in Traditional Networks and SDN Using Time and Frequency Domain Features
This paper examines how residual hardware impairments (RHI) influence the reliability of physical-layer authentication (PLA) in wireless systems enhanced by Reconfigurable Intelligent Surfaces (RIS). The study considers a scenario where a legitimate user and an attacker communicate with a receiver… Read More »Impact of Residual Hardware Impairments on RIS-aided Authentication