Automated Author ProfileBeccari, Marco
Italtel S.p.A. (Italy)
Beccari, Marco
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.7 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
In the context of the NANCY project (https://nancy-project.eu/), this Dataset provides input data for the development of the B-RAN and attacks models for the NANCY framework, to model training and model inference functions. The data collected in the Italian in-lab testbed, namely “dataset 2”, focus on the Edge segment of the network and consider the presence of some of the NANCY components, to assess their impact with respect to the baseline defined through the first collected dataset (D6.6 “Italian in-lab testbed dataset 1”), assessment that will be carried out and completed, together with the technology evaluation, in subsequent project activities. More specifically, the data collected in the Italtel Italian in-lab testbed, where a MEC assisted 5G network scenario with a video streaming application for generating traffic is provided, consider an Edge server based on ARMv8 CPU architecture, and some components specifically developed by the Partners for the NANCY project; these components are integrated in the Italtel environment and are part of the testbed topology set-up: · the “Anomaly detection application module”, provided by CRAT partner (Consortium for Research in Automation and Telecommunications) and focusing on detecting anomalous utilization of computing and network resources· the” VOSySmonitor and vManager”, provided by VOS (Virtual Open Systems), a novel virtualization technology, designed by NANCY, to host offloaded VNFs, which can be deployed in a bare-metal fashion ensuring “application isolation”· “Malicious traffic generation application” and “PAPI extension for ARM performance counter interaction”, provided by SSS (Sant'Anna Higher School of Pisa) for the technology validation in the context of the Italian testbed set-up· “Italtel VTU application”, provided by ITL, which can convert audio and video streams from one format to another, at multiple encodings schemes, changing resolution, bitrate, and video parameters.The dataset contains time series, collected by transmitting video content through the Italtel VTU application. The collected dataset is representative resource-intensive video traffic that has the greatest impact on 5G/B5G network planning and provisioning. The video streaming dataset includes data directly measured while watching the video on the mobile device and data directly measured while generating downstream video stream traversing the gNB (i.e., downstream scenario), and vice versa (i.e., upstream scenario). In each experiment, we fixed the location of the UE and the gNB.Datasets "Band_N3_10MHz_ASP_xxxp_XMbps" and "Band_N78_20MHz_SDP_xxxp_XMbps " are related to Scenario 3 - ARMv8 based edge host: attack condition, same partition (computational resources shared).Dataset “Band_N78_20Mhz_DL RTT+ADC_ASP”, “Band_N78_20Mhz_UL RTT+ADC_ASP”, "Band_N3_10Mhz_DL RTT+ADC_ASP” and "Band_N3_10Mhz_UL RTT+ADC_ASP”, respectively associated with the data captures using bands N78 and N3, contains both RTT (Round Trip Time) and metrics related to resources utilization rates. These last ones, named “Anomaly detection counters” (ADC), are the set of metrics used by the” Anomaly Detection Application”, developed for NANCY by CRAT, to identify the presence or absence of attacks.
Authors
- Beccari, Marco ;
- Clavenna, Antonella ;
- Albanese, Antonino
In the context of the NANCY project (https://nancy-project.eu/), this Dataset provides input data for the development of the B-RAN and attacks models for the NANCY framework, to model training and model inference functions. The data collected in the Italian in-lab testbed, namely “dataset 2”, focus on the Edge segment of the network and consider the presence of some of the NANCY components, to assess their impact with respect to the baseline defined through the first collected dataset (D6.6 “Italian in-lab testbed dataset 1”), assessment that will be carried out and completed, together with the technology evaluation, in subsequent project activities. More specifically, the data collected in the Italtel Italian in-lab testbed, where a MEC assisted 5G network scenario with a video streaming application for generating traffic is provided, consider an Edge server based on ARMv8 CPU architecture, and some components specifically developed by the Partners for the NANCY project; these components are integrated in the Italtel environment and are part of the testbed topology set-up: · the “Anomaly detection application module”, provided by CRAT partner (Consortium for Research in Automation and Telecommunications) and focusing on detecting anomalous utilization of computing and network resources· the” VOSySmonitor and vManager”, provided by VOS (Virtual Open Systems), a novel virtualization technology, designed by NANCY, to host offloaded VNFs, which can be deployed in a bare-metal fashion ensuring “application isolation”· “Malicious traffic generation application” and “PAPI extension for ARM performance counter interaction”, provided by SSS (Sant'Anna Higher School of Pisa) for the technology validation in the context of the Italian testbed set-up· “Italtel VTU application”, provided by ITL, which can convert audio and video streams from one format to another, at multiple encodings schemes, changing resolution, bitrate, and video parameters.The dataset contains time series, collected by transmitting video content through the Italtel VTU application. The collected dataset is representative resource-intensive video traffic that has the greatest impact on 5G/B5G network planning and provisioning. The video streaming dataset includes data directly measured while watching the video on the mobile device and data directly measured while generating downstream video stream traversing the gNB (i.e., downstream scenario), and vice versa (i.e., upstream scenario). In each experiment, we fixed the location of the UE and the gNB.Datasets "Band_N3_10MHz_ASP_xxxp_XMbps" and "Band_N78_20MHz_SDP_xxxp_XMbps " are related to Scenario 3 - ARMv8 based edge host: attack condition, same partition (computational resources shared).Dataset “Band_N78_20Mhz_DL RTT+ADC_ASP”, “Band_N78_20Mhz_UL RTT+ADC_ASP”, "Band_N3_10Mhz_DL RTT+ADC_ASP” and "Band_N3_10Mhz_UL RTT+ADC_ASP”, respectively associated with the data captures using bands N78 and N3, contains both RTT (Round Trip Time) and metrics related to resources utilization rates. These last ones, named “Anomaly detection counters” (ADC), are the set of metrics used by the” Anomaly Detection Application”, developed for NANCY by CRAT, to identify the presence or absence of attacks.
Authors
- Beccari, Marco ;
- Clavenna, Antonella ;
- Albanese, Antonino