A5024747497- Network Security and Intrusion Detection
- Internet Traffic Analysis and Secure E-voting
- Privacy-Preserving Technologies in Data
- Smart Grid Security and Resilience
- Blockchain Technology Applications and Security
- Cybercrime and Law Enforcement Studies
- Anomaly Detection Techniques and Applications
Universidad de Murcia
2021-2023
The application of Machine Learning (ML) techniques to the well-known intrusion detection systems (IDS) is key cope with increasingly sophisticated cybersecurity attacks through an effective and efficient process. In context Internet Things (IoT), most ML-enabled IDS approaches use centralized where IoT devices share their data centers for further analysis. To mitigate privacy concerns associated approaches, in recent years Federated (FL) has attracted a significant interest different...
Federated learning (FL) has attracted significant interest given its prominent advantages and applicability in many scenarios. However, it been demonstrated that sharing updated gradients/weights during the training process can lead to privacy concerns. In context of Internet Things (IoT), this be exacerbated due intrusion detection systems (IDSs), which are intended detect security attacks by analyzing devices' network traffic. Our work provides a comprehensive evaluation differential...
Network Intelligence management in Beyond 5G networks embraces the exciting challenge of addressing scalability, dynamicity, interoperability, privacy, and security concerns. These are essential steps towards achieving realization truly ubiquitous AI-based analytics, empowering seamless integration across entire Continuum (Edge, Fog, Core, Cloud). To address these challenges, this paper presents a model-driven Federated learning approach for managing Orchestrating intelligence needed to...
The popularity of 5G networks has resulted in significant advancement and opportunities connectivity reliability communications, but, concurrently, it raised security challenges privacy concerns due to the distributed highly dynamic nature these networks. In particular, while participating devices nodes a network need be resilient against cyber threats, most them are not allowed exchange their data, and, therefore, they limited only corresponding patterns identified locally. To tackle this,...
The application of Machine Learning (ML) techniques to the well-known intrusion detection systems (IDS) is key cope with increasingly sophisticated cybersecurity attacks through an effective and efficient process. In context Internet Things (IoT), most ML-enabled IDS approaches use centralized where IoT devices share their data centers for further analysis. To mitigate privacy concerns associated approaches, in recent years Federated (FL) has attracted a significant interest different...