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
This paper proposes a lightweight defense mechanism to protect AI models exposed through AI-as-a-Service (AIaaS) from black-box adversarial attacks. As future networks rely more on remotely accessed AI functions, models become vulnerable to malicious queries that subtly manipulate inputs and… Read More »A Novel Method to Mitigate Adversarial Attacks Against AI-as-a-Service Functionality
This paper presents SHERPA, an explainability-driven defense framework designed to protect Federated Learning (FL) systems from data poisoning attacks. FL allows distributed devices to collaboratively train a global model without sharing raw data, but this also opens the door for… Read More »SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks to Defend Against Data Poisoning Attacks
This paper provides a concise overview of how security must evolve as we move toward 6G smart networks, where communication, sensing, and computing become tightly integrated. It highlights that future systems will rely heavily on distributed AI/ML, making the protection… Read More »Advancing Security for 6G Smart Networks and Services
This paper introduces ROBUST-6G, a European research initiative aimed at designing a comprehensive security platform for future 6G networks. As 6G moves toward deeply integrated digital: physical systems, the project focuses on building security mechanisms that are data-driven, automated, trustworthy,… Read More »ROBUST-6G: Smart, Automated, and Reliable Security Service Platform for 6G