In light of the unprecedented proliferation of cloud-native applications, cyber threats targeting cloud services have escalated markedly, emerging as a critical concern for stake-holders. The intrinsic nature of cloud infrastructures renders them particularly susceptible to diverse attacks. In this context, effective attack identification becomes pivotal, facilitating swift responses and preventive measures to mitigate risks and bolster overall security resilience. Despite a range of solutions in literature, attack classification remains an arduous task in industrial environments. To address this, we propose a comprehensive and deployment-friendly graph-based framework. It leverages cloud activity traces, transforming system events to graph structures, and we enumerate 4 different types of attack within a controlled environment. We frame a multi-classification problem, and construct multi-level features to characterize distinct attacks amidst background activities, bypassing the complexity of Deep Learning (DL). To evaluate the efficacy, we compare with multiple Graph Neural Networks (GNNs), and our solution yields comparable performance, demonstrating a promising candidate for the practical and efficient cyber threat detection.

In the past few years, accelerated by Covid-19 pandemic, real-time services based on RTC (Real-Time Communications) have become pervasive, and their performance has become critical for a wide range of business and entertainment applications. The diffusion of RTC services beyond controlled enterprise network perimeters poses the challenge of guaranteeing high-quality, reliable, in-time delivery over the Internet and at global, worldwide scale. In this paper, we address such a problem by proposing ROBUST, a Reliable and Flexible Media Transport based on Pub/Sub Information-Centric Networking (ICN) principles. ROBUST brings enhanced reliability and flexibility to RTC applications over varying network environments and conditions. ROBUST initial design is experimentally assessed on three popular Video Conferencing applications (Jitsi Meet, Cisco Webex, and Zoom) with encouraging improvements in terms of objective Quality of Experience (QoE) metrics: e.g., we observe at least 91% loss recovery in the most challenging experimental settings, an improved video smoothness for Zoom up to 160%, a higher resolution for Webex up to 16%, while Jitsi with ROBUST works in conditions where the original application does not.

Web performance anomalies (e.g. time periods when metrics like page load time are abnormally high) have significant impact on user experience and revenues of web service providers. Existing methods to automatically detect web performance anomalies focus on popular websites (e.g. with tens of thousands of visits per minute). Across a wider diversity of websites, however, the number of visits per hour varies enormously, and some sites will only have few visits per hour. Low rates of visits create measurement gaps and noise that prevent the use of existing methods. This paper develops WMF, a web performance anomaly detection method applicable across a range of websites with highly variable measurement volume. To demonstrate our method, we leverage data from a website monitoring company, which allows us to leverage cross-site measurements. WMF uses matrix factorization to mine patterns that emerge from a subset of the websites to "fill in" missing data on other websites. Our validation using both a controlled website and synthetic anomalies shows that WMF's F1-score is more than double that of the state-of-the-art method. We then apply WMF to three months of web performance measurements to shed light on performance anomalies across a variety of 125 small to medium websites.

Mobility has become a basic premise of network communications, thereby requiring a native integration into 5G networks. Despite numerous efforts to propose and standardize effective mobility-management models for IP, the result is a complex, poorly flexible set of mechanisms. The natural support for mobility offered by information centric networking (ICN) makes it a good candidate to define a radically new solution relieving limitations of the traditional approaches. If consumer mobility is supported in ICN by design, in virtue of its connectionless pull-based communication model, producer mobility is still an open challenge. In this paper, we look at two prominent ICN architectures, content centric networking (CCN) and named data networking (NDN) and we propose MAP-Me, an anchor-less solution to manage micro-mobility of content producers via a name-based CCN/NDN data plane, with support for latency-sensitive streaming applications. We analyze MAP-Me performance and provide guarantees of correctness, stability, and bounded stretch, which we verify on real ISP topologies. Finally, we set up a comprehensive simulation environment in NDNSim 2.1 for MAP-Me evaluation and comparison against the existing classes of solutions, including a realistic trace-driven car-mobility pattern under a 802.11n radio access. The results are encouraging and highlight the superiority of MAP-Me in terms of user performance and network cost metrics. All the code is available as open-source.

In this work we propose ParkMaster, a low-cost crowdsourcing architecture which exploits machine learning techniques and vision algorithms to evaluate parking availability in cities. While the user is normally driving ParkMaster enables off the shelf smartphones to collect information about the presence of parked vehicles by running image recognition techniques on the phones camera video streaming. The paper describes the design of ParkMaster's architecture and shows the feasibility of deploying such mobile sensor system in nowadays smartphones, in particular focusing on the practicability of running vision algorithms on phones.

The need for Internet access from moving vehicles has been steadily increasing in the past few years. Solutions that rely on cellular connectivity are becoming impractical to deploy due to technical and economic reasons. Car-Fi proposes an approach that leverages existing home Wi-Fi access points configured in dual-access mode, in order to offload all data traffic from the congested and expensive cellular infrastructure to whatever Wi-Fi network is available. Thanks to an improved scanning algorithm and numerous optimizations to the connection setup, Car-Fi makes downloading large amounts of data from a moving car feasible.

This paper proposes Navigo, a location based packet forwarding mechanism for vehicular Named Data Networking (NDN). Navigo takes a radically new approach to address the challenges of frequent connectivity disruptions and sudden network changes in a vehicle network. Instead of forwarding packets to a specific moving car, Navigo aims to fetch specific pieces of data from multiple potential carriers of the data. The design provides (1) a mechanism to bind NDN data names to the producers' geographic area(s); (2) an algorithm to guide Interests towards data producers using a specialized shortest path over the road topology; and (3) an adaptive discovery and selection mechanism that can identify the best data source across multiple geographic areas, as well as quickly react to changes in the V2X network.