Author

A. Marotta

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Open access 2026

From Ground to Air: Making Mobile Networks Ready for Urban Air Mobility

Urban Air Mobility (UAM) is expected to be an innovative component of future transportation systems. It introduces great flexibility (additional degrees of freedom) to mobility in 3D space and paves the way to novel design methodologies of wireless communication networks to support both critical and non-critical communications. Main requirements for these networks are high reliability, low latency, and high throughput, barely addressable with existing terrestrial networks unless these are upgraded on the basis of a thorough assessment of coverage capabilities and network planning methodologies. This work investigates the suitability of modern mobile networks, such as 5G and forthcoming 6G, to enable the delicate UAM operations and identifies the gaps to be filled in. The study introduces an enhanced evaluation framework based on a system-level simulator tailored to UAM-specific characteristics in realistic scenarios. Our methodology allows us to determine the minimum number of enhanced Base Stations (eBSs) that must be upgraded to satisfy the considered throughput and BLock Error Rate (BLER) requirements under the adopted system assumptions. The framework integrates a channel-aware selection strategy that prioritizes the upgrades in the subset of base stations that allow the provisioning of the most favorable aggregated link conditions. We explore a variety of realistic deployment scenarios, analyzing the impact of parameters such as UAM Vehicle (UV) population, performance requirements, transmit power, and target BLER on achievable network performance. The results highlight the trade-offs between infrastructure density and service quality, offering practical guidelines for network operators to design UAM-ready deployments along the evolution path toward 6G systems.

Alex Piccioni, A. Marotta, Claudia Rinaldi et al. · IEEE Transactions on Network... · 0 citations

Conference Aug 2026

Real-Time DDoS Detection by Integrated eBPF Telemetry and Machine Learning-enhanced SIEM

Distributed Denial-of-Service (DDoS) attacks remain one of the most disruptive threats to modern web services, overwhelming application resources and degrading service availability. This paper presents a lightweight, virtualized system architecture for real-time DDoS detection that combines kernellevel telemetry collection with machine learning (ML) based analysis. The proposed architecture enables fine-grained, lowoverhead log collection without modifying the web applications because the network and application-level events generated during normal and attack traffic are captured directly at the kernel layer by means of an extended Berkeley Packet Filter (eBPF). The collected logs are then processed within a Security Information and Event Management (SIEM) platform, where ML–based detection models analyze traffic patterns and behavioral features to identify DDoS attacks in near real-time. This architecture improves visibility into attack characteristics while maintaining minimal performance impact on the protected services. The proposed system demonstrates how eBPF-based observability, when integrated with SIEM and ML techniques, can provide an effective, scalable, and modular approach for DDoS detection in virtualized environments. The design is particularly suited for cloud and multi-VM deployments, offering enhanced security monitoring, faster attack detection, and improved operational resilience.

Zeeshan Ali, A. Marotta, W. Tiberti et al. · 2026 International Conferenc... · 0 citations