This paper presents a machine learning–driven localization framework that leverages Angle of Arrival (AoA) as a robust feature extracted from massive MIMO OFDM channel state information (CSI). The goal is to enable accurate user or device localization in complex outdoor… Read More »High-accuracy AoA-based Localization using Hierarchical ML Classifiers in Outdoor Environments
This paper investigates whether Angle of Arrival (AoA) can serve as a reliable feature for physical-layer authentication (PLA), particularly when an adversary attempts to impersonate a legitimate transmitter. While previous PLA methods often rely on features such as channel frequency… Read More »Leveraging Angle of Arrival Estimation against Impersonation Attacks in Physical Layer Authentication
This paper introduces DRACO, a decentralized and asynchronous federated learning framework tailored for wireless networks where communication delays, node heterogeneity, and topology variations make traditional synchronous FL inefficient. Unlike centralized approaches, DRACO removes the need for a server and allows… Read More »DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networks
This paper introduces S-VOTE, a similarity-based voting mechanism designed to improve both efficiency and model performance in Decentralized Federated Learning (DFL). Unlike traditional federated learning, DFL operates without a central server, relying on peer-to-peer communication. While this avoids bottlenecks and… Read More »S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning
This paper presents HyperDtct, a hypervisor-based framework for detecting ransomware by monitoring system call behavior from outside the guest operating system. Rather than relying on in-guest agents or signature-based methods, both of which can be evaded by modern ransomware, HyperDtct… Read More »HyperDtct: Hypervisor-Based Ransomware Detection using System Calls
This paper introduces ProFe, a new algorithm designed to make Decentralized Federated Learning (DFL) more communication-efficient without compromising model performance. In DFL, clients collaborate without a central server, which avoids single-point failures but creates significant communication overhead—especially when nodes have… Read More »ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes
This paper examines how future 6G networks—expected to function as large-scale cyber-physical systems—will require more advanced trust and reputation management mechanisms than those used today. As autonomous vehicles, drones, robots, and other intelligent agents collaborate in real time, the accuracy… Read More »A Framework for Global Trust and Reputation Management in 6G Networks
This paper studies how to keep remote estimators informed about the state of a Markov source while minimizing two critical metrics: the Age of Missed Alarms (AoMA) and the Age of False Alarms (AoFA). These ages quantify how long an… Read More »Minimizing the Age of Missed and False Alarms in RemoteEstimation of Markov Sources
This paper examines how Reconfigurable Intelligent Surfaces (RIS) can be used to strengthen wireless challenge–response physical-layer authentication (CR-PLA). In CR-PLA, the receiver verifies a transmitter by sending a challenge and checking whether the resulting channel response matches the expected physical… Read More »Analysis of Challenge-Response Authentication With Reconfigurable Intelligent Surfaces
This paper explores a machine-learning approach for detecting jamming attacks in private 5G networks by framing the problem as one-class classification. Rather than relying on prior knowledge of attacker behavior, the authors aim to detect whether a received signal deviates… Read More »One-Class Classification as GLRT for Jamming Detection in Private 5G Networks