Valerio Persico

Giuseppe Aceto, Valerio Persico, Antonio Pescapè

This paper analyzes the Internet shutdown that occurred in Iran in January 2026 in the context of protests, focusing on its impact on the country's digital communication infrastructure and on information access and control dynamics. The scale, complexity, and nation-state nature of the event motivate a comprehensive investigation that goes beyond isolated reports, aiming to provide a unified and systematic understanding of what happened and how it was observed. The study is guided by a set of research questions addressing: the characterization of the shutdown via the timeline of the disruption events and post-event "new normal"; the detectability of the event, encompassing monitoring initiatives, measurement techniques, and precursory signals; and the interplay between censorship and circumvention, assessing both the imposed restrictions and the effectiveness of tools designed to bypass them. To answer these questions, we adopt a multi-source, multi-perspective methodology that integrates heterogeneous public data, primarily from grey literature produced by network measurement and monitoring initiatives, complemented by additional private measurements. This approach enables a holistic view of the event and allows us to reconcile and compare partial observations from different sources.

Giampaolo Bovenzi, Francesco Cerasuolo, Domenico Ciuonzo et al.

Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and Diffusion Models have recently gained widespread attention from both the research and the industrial communities. This survey explores their application in network monitoring and management, focusing on prominent use cases, as well as challenges and opportunities. We discuss how network traffic generation and classification, network intrusion detection, networked system log analysis, and network digital assistance can benefit from the use of GenAI models. Additionally, we provide an overview of the available GenAI models, datasets for large-scale training phases, and platforms for the development of such models. Finally, we discuss research directions that potentially mitigate the roadblocks to the adoption of GenAI for network monitoring and management. Our investigation aims to map the current landscape and pave the way for future research in leveraging GenAI for network monitoring and management.