| Nome |
# |
|
Machine Learning and Big Data Methodologies for Network Traffic Monitoring, file e384c42f-656d-d4b2-e053-9f05fe0a1d67
|
1.347
|
|
Five Years at the Edge: Watching Internet From the ISP Network, file e384c431-9094-d4b2-e053-9f05fe0a1d67
|
749
|
|
SeLINA: a Self-Learning Insightful Network Analyzer, file e384c42f-13a8-d4b2-e053-9f05fe0a1d67
|
470
|
|
Users’ Fingerprinting Techniques from TCP Traffic, file e384c42f-804a-d4b2-e053-9f05fe0a1d67
|
357
|
|
BGPStream: A Software Framework for Live and Historical BGP Data Analysis, file e384c42f-3878-d4b2-e053-9f05fe0a1d67
|
268
|
|
A First Characterization of Anycast Traffic from Passive Traces, file e384c42f-21f1-d4b2-e053-9f05fe0a1d67
|
250
|
|
UMAP: Urban Mobility Analysis Platform to Harvest Car Sharing Data, file e384c42f-8661-d4b2-e053-9f05fe0a1d67
|
233
|
|
YouLighter: A Cognitive Approach to Unveil YouTube CDN and Changes, file e384c42e-d5fe-d4b2-e053-9f05fe0a1d67
|
198
|
|
Exploring Browsing Habits of Internauts, file e384c42e-d5ff-d4b2-e053-9f05fe0a1d67
|
186
|
|
Network Highlighter, file e384c42f-8f6b-d4b2-e053-9f05fe0a1d67
|
169
|
|
YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes, file e384c42e-5cf9-d4b2-e053-9f05fe0a1d67
|
167
|
|
Free Floating Electric Car Sharing in Smart Cities: Data Driven System Dimensioning, file e384c430-395c-d4b2-e053-9f05fe0a1d67
|
140
|
|
Measuring HTTP/3: Adoption and Performance, file e384c434-15be-d4b2-e053-9f05fe0a1d67
|
122
|
|
E-Scooter Sharing: Leveraging Open Data for System Design, file e384c432-6f82-d4b2-e053-9f05fe0a1d67
|
98
|
|
Unsupervised methodology to unveil content delivery network structures, file e384c42f-9105-d4b2-e053-9f05fe0a1d67
|
92
|
|
Data Driven Optimization of Charging Station Placement for EV Free Floating Car Sharing, file e384c430-9be3-d4b2-e053-9f05fe0a1d67
|
91
|
|
The Exploitation of Web Navigation Data: Ethical Issues and Alternative Scenarios, file e384c42f-065c-d4b2-e053-9f05fe0a1d67
|
88
|
|
A machine learning application for latency prediction in operational 4G networks, file e384c431-2b6d-d4b2-e053-9f05fe0a1d67
|
85
|
|
Impact of Charging Infrastructure and Policies on Electric Car Sharing Systems, file e384c432-c427-d4b2-e053-9f05fe0a1d67
|
85
|
|
Predicting Car Availability in Free Floating Car Sharing Systems: Leveraging Machine Learning in Challenging Contexts, file e384c432-811f-d4b2-e053-9f05fe0a1d67
|
73
|
|
Free Floating Electric Car Sharing: A Data Driven Approach for System Design, file e384c431-184c-d4b2-e053-9f05fe0a1d67
|
72
|
|
On Scalability of Electric Car Sharing in Smart Cities, file e384c432-8d9d-d4b2-e053-9f05fe0a1d67
|
72
|
|
YouLighter, file e384c42f-88d4-d4b2-e053-9f05fe0a1d67
|
61
|
|
Realistic testing of RTC applications under mobile networks, file e384c432-940a-d4b2-e053-9f05fe0a1d67
|
52
|
|
Dissecting a Data-driven Prognostic Pipeline: A Powertrain use case, file e384c433-5ac2-d4b2-e053-9f05fe0a1d67
|
48
|
|
Environmental and Economic Comparison of ICEV and EV in Car Sharing, file e384c434-2887-d4b2-e053-9f05fe0a1d67
|
45
|
|
Benefits of Relocation on E-scooter Sharing - a Data-Informed Approach, file e384c434-5cc8-d4b2-e053-9f05fe0a1d67
|
41
|
|
An Open Dataset of Operational Mobile Networks, file e384c432-cb52-d4b2-e053-9f05fe0a1d67
|
39
|
|
Data-driven exploratory models of an electric distribution network for fault prediction and diagnosis, file e384c431-235c-d4b2-e053-9f05fe0a1d67
|
38
|
|
A Network Analysis on Cloud Gaming: Stadia, GeForce Now and PSNow, file e384c434-4bac-d4b2-e053-9f05fe0a1d67
|
36
|
|
K-MDTSC: K-Multi-Dimensional Time-Series Clustering Algorithm, file e384c433-515e-d4b2-e053-9f05fe0a1d67
|
22
|
|
Data-Driven Emulation of Mobile Access Networks, file e384c432-7c4f-d4b2-e053-9f05fe0a1d67
|
18
|
|
The New Abnormal: Network Anomalies in the AI Era, file e384c433-d0ec-d4b2-e053-9f05fe0a1d67
|
16
|
|
Message from the organizers of WAIN, file e384c434-522b-d4b2-e053-9f05fe0a1d67
|
15
|
|
Free floating electric car sharing design: Data driven optimisation, file e384c430-664c-d4b2-e053-9f05fe0a1d67
|
14
|
|
Free floating electric car sharing design: Data driven optimisation, file e384c433-e180-d4b2-e053-9f05fe0a1d67
|
13
|
|
BGPStream: A Software Framework for Live and Historical BGP Data Analysis, file e384c42f-2f4d-d4b2-e053-9f05fe0a1d67
|
8
|
|
What Scanners do at L7? Exploring Horizontal Honeypots for Security Monitoring, file 65aeb54d-e4fc-44d1-accd-722701671575
|
7
|
|
SeLINA: a Self-Learning Insightful Network Analyzer, file e384c42f-2c56-d4b2-e053-9f05fe0a1d67
|
7
|
|
Users’ Fingerprinting Techniques from TCP Traffic, file e384c42f-8660-d4b2-e053-9f05fe0a1d67
|
7
|
|
Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case, file e384c434-5812-d4b2-e053-9f05fe0a1d67
|
7
|
|
A first look at starlink performance, file bb117d3d-877f-4116-953f-5f80f1f5cc79
|
6
|
|
Dissecting a Data-driven Prognostic Pipeline: A Powertrain use case, file e384c433-5ac1-d4b2-e053-9f05fe0a1d67
|
6
|
|
Demand Model Generation from Traces: Adaptive KDE Data-Driven Optimization, file 7216b70e-8881-4969-84ab-481b0f241d99
|
5
|
|
SeLINA: a Self-Learning Insightful Network Analyzer, file e384c42f-0dbd-d4b2-e053-9f05fe0a1d67
|
5
|
|
Dissecting a Data-driven Prognostic Pipeline: A Powertrain use case, file e384c433-664a-d4b2-e053-9f05fe0a1d67
|
5
|
|
Data-driven exploratory models of an electric distribution network for fault prediction and diagnosis, file e384c431-5482-d4b2-e053-9f05fe0a1d67
|
4
|
|
ERRANT: Realistic Emulation of Radio Access Networks, file e384c432-2174-d4b2-e053-9f05fe0a1d67
|
4
|
|
A multi-faceted characterization of free-floating car sharing service usage, file e384c433-1097-d4b2-e053-9f05fe0a1d67
|
4
|
|
Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case, file e384c434-224f-d4b2-e053-9f05fe0a1d67
|
4
|
|
A first look at HTTP/3 adoption and performance, file e384c434-851d-d4b2-e053-9f05fe0a1d67
|
4
|
|
Demand Model Generation from Traces: Adaptive KDE Data-Driven Optimization, file 632227c3-9379-4a7a-a968-21c7790b7f7e
|
3
|
|
When satellite is all you have: watching the internet from 550 ms, file 9470cbcc-7646-4c05-9a67-072930e57ae2
|
3
|
|
ERRANT: Realistic Emulation of Radio Access Networks, file e384c431-ee5e-d4b2-e053-9f05fe0a1d67
|
3
|
|
Five Years at the Edge: Watching Internet From the ISP Network, file e384c431-fd2a-d4b2-e053-9f05fe0a1d67
|
3
|
|
E-Scooter Sharing: Leveraging Open Data for System Design, file e384c432-6f83-d4b2-e053-9f05fe0a1d67
|
3
|
|
Data-Driven Emulation of Mobile Access Networks, file e384c432-854b-d4b2-e053-9f05fe0a1d67
|
3
|
|
Debate on online social networks at the time of COVID-19: An Italian case study, file e384c433-887b-d4b2-e053-9f05fe0a1d67
|
3
|
|
Measuring HTTP/3: Adoption and Performance, file e384c434-15bd-d4b2-e053-9f05fe0a1d67
|
3
|
|
Data Driven Scalability and Profitability Analysis in Free Floating Electric Car Sharing Systems, file 09f45681-8d75-48c0-ab9e-3ce9ec6de039
|
2
|
|
Legal Entity Disambiguation for Financial Crime Detection, file 2d04158b-a342-4a29-9b78-a0c1441f33fc
|
2
|
|
GLEm-Net: Unified Framework for Data Reduction with Categorical and Numerical Features, file 2f2f6ea4-89ae-4045-99b6-88373c88feba
|
2
|
|
Data Driven Scalability and Profitability Analysis in Free Floating Electric Car Sharing Systems, file a825c01a-ba69-47a2-b24b-2cadbbaa6be2
|
2
|
|
GLEm-Net: Unified Framework for Data Reduction with Categorical and Numerical Features, file e121a11c-f825-4571-b03f-b025a5fa2c39
|
2
|
|
Data Driven Optimization of Charging Station Placement for EV Free Floating Car Sharing, file e384c430-72fe-d4b2-e053-9f05fe0a1d67
|
2
|
|
Data-Driven Emulation of Mobile Access Networks, file e384c432-6736-d4b2-e053-9f05fe0a1d67
|
2
|
|
A first look at HTTP/3 adoption and performance, file e384c434-851e-d4b2-e053-9f05fe0a1d67
|
2
|
|
Message from the organizers of WAIN, file e384c434-e66c-d4b2-e053-9f05fe0a1d67
|
2
|
|
What Scanners do at L7? Exploring Horizontal Honeypots for Security Monitoring, file e384c434-e7b0-d4b2-e053-9f05fe0a1d67
|
2
|
|
LogPrécis: Unleashing language models for automated malicious log analysis, file 2348bc13-616a-41dc-9511-05c65c9e9630
|
1
|
|
Legal Entity Disambiguation for Financial Crime Detection, file 5c68b16c-1fad-48ce-ae85-75a717f03a89
|
1
|
|
Monitoring Web QoE in Satellite Networks from Passive Measurements, file 70a9581c-e512-4402-8635-1354661b7250
|
1
|
|
A first look at starlink performance, file 805e7678-9875-43e2-9961-dc1afa20c35f
|
1
|
|
Monitoring Web QoE in Satellite Networks from Passive Measurements, file 9795e982-89aa-4a33-842d-43d7cbb87a55
|
1
|
|
Enlightening the Darknets: Augmenting Darknet Visibility with Active Probes, file a2eabd41-33ba-4322-9c68-cec01d431690
|
1
|
|
Exploring Browsing Habits of Internauts, file e384c42e-d310-d4b2-e053-9f05fe0a1d67
|
1
|
|
A machine learning application for latency prediction in operational 4G networks, file e384c431-6f37-d4b2-e053-9f05fe0a1d67
|
1
|
|
On Scalability of Electric Car Sharing in Smart Cities, file e384c432-8d9e-d4b2-e053-9f05fe0a1d67
|
1
|
|
Realistic testing of RTC applications under mobile networks, file e384c432-aea0-d4b2-e053-9f05fe0a1d67
|
1
|
|
Impact of Charging Infrastructure and Policies on Electric Car Sharing Systems, file e384c432-c428-d4b2-e053-9f05fe0a1d67
|
1
|
|
An Open Dataset of Operational Mobile Networks, file e384c432-ccb2-d4b2-e053-9f05fe0a1d67
|
1
|
|
Free Floating Electric Car Sharing: A Data Driven Approach for System Design, file e384c432-f76c-d4b2-e053-9f05fe0a1d67
|
1
|
|
A multi-faceted characterization of free-floating car sharing service usage, file e384c433-3bd6-d4b2-e053-9f05fe0a1d67
|
1
|
|
A multi-faceted characterization of free-floating car sharing service usage, file e384c433-3bd7-d4b2-e053-9f05fe0a1d67
|
1
|
|
Debate on online social networks at the time of COVID-19: An Italian case study, file e384c433-7b39-d4b2-e053-9f05fe0a1d67
|
1
|
|
Environmental and Economic Comparison of ICEV and EV in Car Sharing, file e384c434-56d0-d4b2-e053-9f05fe0a1d67
|
1
|
|
Benefits of Relocation on E-scooter Sharing - a Data-Informed Approach, file e384c434-5a8a-d4b2-e053-9f05fe0a1d67
|
1
|
|
Enlightening the Darknets: Augmenting Darknet Visibility with Active Probes, file f1742302-a34a-40ee-816e-4ce5d066922b
|
1
|
| Totale |
6.016 |
Archivio Istituzionale della Ricerca