| Nome |
# |
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Inside Dropbox: Understanding Personal Cloud Storage Services, file e384c42e-2614-d4b2-e053-9f05fe0a1d67
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2434
|
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Personal cloud storage: Usage, performance and impact of terminals, file e384c42e-5cfc-d4b2-e053-9f05fe0a1d67
|
1606
|
|
Flow monitoring explained: From packet capture to data analysis with NetFlow and IPFIX, file e384c42f-45ad-d4b2-e053-9f05fe0a1d67
|
1265
|
|
Impact of Access Line Capacity on Adaptive Video Streaming Quality - A Passive Perspective, file e384c42f-3741-d4b2-e053-9f05fe0a1d67
|
1032
|
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Benchmarking personal cloud storage, file e384c42e-296d-d4b2-e053-9f05fe0a1d67
|
820
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Five Years at the Edge: Watching Internet From the ISP Network, file e384c431-9094-d4b2-e053-9f05fe0a1d67
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742
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Personal Cloud Storage Benchmarks and Comparison, file e384c42f-7aa2-d4b2-e053-9f05fe0a1d67
|
682
|
|
Five Years at the Edge: Watching Internet from the ISP Network, file e384c430-7556-d4b2-e053-9f05fe0a1d67
|
627
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Experiences of Cloud Storage Service Monitoring: Performance Assessment and Comparison, file e384c42e-d30a-d4b2-e053-9f05fe0a1d67
|
536
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Modeling the Dropbox client behavior, file e384c42f-42e8-d4b2-e053-9f05fe0a1d67
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402
|
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Detecting user actions from HTTP traces: Toward an automatic approach, file e384c42f-2cdc-d4b2-e053-9f05fe0a1d67
|
329
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PAIN: A Passive Web Speed Indicator for ISPs, file e384c42f-8fea-d4b2-e053-9f05fe0a1d67
|
307
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Automatic bearing fault pattern recognition using vibration signal analysis, file e384c42f-48d6-d4b2-e053-9f05fe0a1d67
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301
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Report from the 6th PhD School on Traffic Monitoring and Analysis (TMA), file e384c42f-4ae2-d4b2-e053-9f05fe0a1d67
|
268
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Understanding and Monitoring Cloud Services, file e384c42f-4402-d4b2-e053-9f05fe0a1d67
|
265
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Flow monitoring experiences at the ethernet-layer, file e384c42f-4341-d4b2-e053-9f05fe0a1d67
|
261
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Automatic detection of DNS manipulations, file e384c42f-df88-d4b2-e053-9f05fe0a1d67
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261
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AWESoME: Big Data for Automatic Web Service Management in SDN, file e384c42f-e73d-d4b2-e053-9f05fe0a1d67
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255
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A multi-measure nearest neighbor algorithm for time series classification, file e384c42f-48d7-d4b2-e053-9f05fe0a1d67
|
252
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Robust URL Classification With Generative Adversarial Networks, file e384c430-be9d-d4b2-e053-9f05fe0a1d67
|
238
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Scalable service performance monitoring, file e384c42f-4343-d4b2-e053-9f05fe0a1d67
|
233
|
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Towards web service classification using addresses and DNS, file e384c42f-32fe-d4b2-e053-9f05fe0a1d67
|
223
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Report of the third workshop on the usage of NetFlow/IPFIX in network management, file e384c42f-4b2f-d4b2-e053-9f05fe0a1d67
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215
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Carrier ethernet OAM: An overview and comparison to IP OAM, file e384c42f-48d5-d4b2-e053-9f05fe0a1d67
|
208
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WHAT: A Big Data Approach for Accounting of Modern Web Services, file e384c42f-83c4-d4b2-e053-9f05fe0a1d67
|
208
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Report of the second workshop on the usage of NetFlow/IPFIX in network management, file e384c42f-4d40-d4b2-e053-9f05fe0a1d67
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203
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HPC4AI, an AI-on-demand federated platform endeavour, file e384c430-444f-d4b2-e053-9f05fe0a1d67
|
122
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Measuring HTTP/3: Adoption and Performance, file e384c434-15be-d4b2-e053-9f05fe0a1d67
|
120
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DPI Solutions in Practice: Benchmark and Comparison, file e384c433-c645-d4b2-e053-9f05fe0a1d67
|
96
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Does domain name encryption increase users' privacy?, file e384c432-5a83-d4b2-e053-9f05fe0a1d67
|
79
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You, the Web, and Your Device: Longitudinal Characterization of Browsing Habits, file e384c430-6186-d4b2-e053-9f05fe0a1d67
|
58
|
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Is There a Case for Parallel Connections with Modern Web Protocols?, file e384c430-bb82-d4b2-e053-9f05fe0a1d67
|
53
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|
Unveiling Community Dynamics on Instagram Political Network, file e384c432-326d-d4b2-e053-9f05fe0a1d67
|
53
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PAIN: A Passive Web Performance Indicator for ISPs, file e384c430-a0f2-d4b2-e053-9f05fe0a1d67
|
46
|
|
A Survey on Big Data for Network Traffic Monitoring and Analysis, file e384c430-f71f-d4b2-e053-9f05fe0a1d67
|
46
|
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α-MON: Traffic Anonymizer for Passive Monitoring, file e384c432-d8e6-d4b2-e053-9f05fe0a1d67
|
46
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Sensing the Noise: Uncovering Communities in Darknet Traffic, file e384c432-8694-d4b2-e053-9f05fe0a1d67
|
45
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Workload models and performance evaluation of cloud storage services, file e384c42f-2b9c-d4b2-e053-9f05fe0a1d67
|
44
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Privacy-preserving network monitoring at high-speed, file e384c431-3e79-d4b2-e053-9f05fe0a1d67
|
34
|
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Characterizing Usage Patterns and Service Demand of a Two-Way Car-Sharing System, file e384c430-add4-d4b2-e053-9f05fe0a1d67
|
31
|
|
Towards Understanding Political Interactions on Instagram, file e384c431-56c2-d4b2-e053-9f05fe0a1d67
|
31
|
|
Augmenting phishing squatting detection with GANs, file e384c434-0cc0-d4b2-e053-9f05fe0a1d67
|
31
|
|
Experiences of Cloud Storage Service Monitoring: Performance Assessment and Comparison, file e384c42e-d30b-d4b2-e053-9f05fe0a1d67
|
24
|
|
RL-IoT: Reinforcement Learning to Interact with IoT Devices, file e384c433-f031-d4b2-e053-9f05fe0a1d67
|
23
|
|
α-MON: Anonymized Passive Traffic Monitoring, file e384c433-3020-d4b2-e053-9f05fe0a1d67
|
22
|
|
DarkVec: automatic analysis of darknet traffic with word embeddings, file e384c434-bc9b-d4b2-e053-9f05fe0a1d67
|
22
|
|
Measuring Web Speed From Passive Traces, file e384c430-318e-d4b2-e053-9f05fe0a1d67
|
21
|
|
Improving Performance of QUIC in WiFi, file e384c432-6a7a-d4b2-e053-9f05fe0a1d67
|
21
|
|
The New Abnormal: Network Anomalies in the AI Era, file e384c433-d0ec-d4b2-e053-9f05fe0a1d67
|
15
|
|
HPC4AI, an AI-on-demand federated platform endeavour, file e384c430-33e3-d4b2-e053-9f05fe0a1d67
|
10
|
|
Are Darknets All The Same? On Darknet Visibility for Security Monitoring, file e384c431-702e-d4b2-e053-9f05fe0a1d67
|
10
|
|
Does domain name encryption increase users' privacy?, file e384c432-5109-d4b2-e053-9f05fe0a1d67
|
9
|
|
Characterizing and Modeling the Dropbox Workload, file e384c42f-4cac-d4b2-e053-9f05fe0a1d67
|
7
|
|
Campus Traffic and e-Learning during COVID-19 Pandemic, file e384c432-241a-d4b2-e053-9f05fe0a1d67
|
7
|
|
On the dynamics of political discussions on Instagram: A network perspective, file e384c433-a536-d4b2-e053-9f05fe0a1d67
|
7
|
|
Characterizing QoE in Large-Scale Live Streaming, file e384c42f-eb26-d4b2-e053-9f05fe0a1d67
|
6
|
|
How HTTP/2 is changing web traffic and how to detect it, file e384c42f-f2dd-d4b2-e053-9f05fe0a1d67
|
6
|
|
Cost-Benefit Tradeoffs of Content Sharing in Personal Cloud Storage, file e384c42f-f304-d4b2-e053-9f05fe0a1d67
|
6
|
|
You, the Web, and Your Device: Longitudinal Characterization of Browsing Habits, file e384c430-5c40-d4b2-e053-9f05fe0a1d67
|
6
|
|
DarkVec: automatic analysis of darknet traffic with word embeddings, file e384c434-135a-d4b2-e053-9f05fe0a1d67
|
6
|
|
AI-based Sound-Squatting Attack Made Possible, file 1dd2efbb-9eed-4db5-be4f-ecfcc362572c
|
5
|
|
What Scanners do at L7? Exploring Horizontal Honeypots for Security Monitoring, file 65aeb54d-e4fc-44d1-accd-722701671575
|
5
|
|
Automatic detection of DNS manipulations, file e384c42f-df89-d4b2-e053-9f05fe0a1d67
|
5
|
|
Robust URL Classification With Generative Adversarial Networks, file e384c430-7561-d4b2-e053-9f05fe0a1d67
|
5
|
|
Automatic rule generation for flow management in software defined networking networks, file e384c430-b045-d4b2-e053-9f05fe0a1d67
|
5
|
|
Towards NLP-based Processing of Honeypot Logs, file e384c434-c9ee-d4b2-e053-9f05fe0a1d67
|
5
|
|
Impact of Access Line Capacity on Adaptive Video Streaming Quality - A Passive Perspective, file e384c42f-39b8-d4b2-e053-9f05fe0a1d67
|
4
|
|
PAIN: A Passive Web Speed Indicator for ISPs, file e384c42f-8fe9-d4b2-e053-9f05fe0a1d67
|
4
|
|
Five Years at the Edge: Watching Internet from the ISP Network, file e384c430-8f09-d4b2-e053-9f05fe0a1d67
|
4
|
|
Towards Understanding Political Interactions on Instagram, file e384c431-56c3-d4b2-e053-9f05fe0a1d67
|
4
|
|
A first look at HTTP/3 adoption and performance, file e384c434-851d-d4b2-e053-9f05fe0a1d67
|
4
|
|
PAIN: A Passive Web Performance Indicator for ISPs, file e384c430-7a0e-d4b2-e053-9f05fe0a1d67
|
3
|
|
Five Years at the Edge: Watching Internet From the ISP Network, file e384c431-fd2a-d4b2-e053-9f05fe0a1d67
|
3
|
|
DPI Solutions in Practice: Benchmark and Comparison, file e384c433-fe3e-d4b2-e053-9f05fe0a1d67
|
3
|
|
Measuring HTTP/3: Adoption and Performance, file e384c434-15bd-d4b2-e053-9f05fe0a1d67
|
3
|
|
Big Data in Computer Network Monitoring, file 5a581da0-e12c-4664-81cb-7d42d9af2a94
|
2
|
|
Towards web service classification using addresses and DNS, file e384c42f-3126-d4b2-e053-9f05fe0a1d67
|
2
|
|
Analysing Costs and Benefits of Content Sharing in Cloud Storage, file e384c42f-45ae-d4b2-e053-9f05fe0a1d67
|
2
|
|
The Impact of Content Sharing on Cloud Storage Bandwidth Consumption, file e384c42f-45af-d4b2-e053-9f05fe0a1d67
|
2
|
|
Measurement Artifacts in NetFlow Data, file e384c42f-45f0-d4b2-e053-9f05fe0a1d67
|
2
|
|
The Impact of Content Sharing on Cloud Storage Bandwidth Consumption, file e384c42f-cff0-d4b2-e053-9f05fe0a1d67
|
2
|
|
Big Data in Computer Network Monitoring, file e384c430-7c20-d4b2-e053-9f05fe0a1d67
|
2
|
|
A Survey on Big Data for Network Traffic Monitoring and Analysis, file e384c431-3e64-d4b2-e053-9f05fe0a1d67
|
2
|
|
Unveiling Community Dynamics on Instagram Political Network, file e384c432-8577-d4b2-e053-9f05fe0a1d67
|
2
|
|
RL-IoT: Reinforcement Learning to Interact with IoT Devices, file e384c433-b6a3-d4b2-e053-9f05fe0a1d67
|
2
|
|
On the dynamics of political discussions on Instagram: A network perspective, file e384c434-3509-d4b2-e053-9f05fe0a1d67
|
2
|
|
What Scanners do at L7? Exploring Horizontal Honeypots for Security Monitoring, file e384c434-e7b0-d4b2-e053-9f05fe0a1d67
|
2
|
|
AI-based Sound-Squatting Attack Made Possible, file e038e1fe-ad4a-4a27-bb4a-66cd5404c494
|
1
|
|
Detecting user actions from HTTP traces: Toward an automatic approach, file e384c42f-2c61-d4b2-e053-9f05fe0a1d67
|
1
|
|
Measuring Cloud Service Health Using NetFlow/IPFIX: The WikiLeaks Case, file e384c42f-42e9-d4b2-e053-9f05fe0a1d67
|
1
|
|
Analyzing the Impact of Dropbox Content Sharing on an Academic Network, file e384c42f-4342-d4b2-e053-9f05fe0a1d67
|
1
|
|
WHAT: A Big Data Approach for Accounting of Modern Web Services, file e384c42f-81ec-d4b2-e053-9f05fe0a1d67
|
1
|
|
The curious case of parallel connections in HTTP/2, file e384c42f-96a3-d4b2-e053-9f05fe0a1d67
|
1
|
|
Campus Traffic and e-Learning during COVID-19 Pandemic, file e384c432-2c05-d4b2-e053-9f05fe0a1d67
|
1
|
|
Sensing the Noise: Uncovering Communities in Darknet Traffic, file e384c432-5e64-d4b2-e053-9f05fe0a1d67
|
1
|
|
α-MON: Anonymized Passive Traffic Monitoring, file e384c433-29a0-d4b2-e053-9f05fe0a1d67
|
1
|
|
AWESoME: Big Data for Automatic Web Service Management in SDN, file e384c433-2e38-d4b2-e053-9f05fe0a1d67
|
1
|
|
Augmenting phishing squatting detection with GANs, file e384c434-27ea-d4b2-e053-9f05fe0a1d67
|
1
|
|
A first look at HTTP/3 adoption and performance, file e384c434-851e-d4b2-e053-9f05fe0a1d67
|
1
|
|
Towards NLP-based Processing of Honeypot Logs, file e384c434-d556-d4b2-e053-9f05fe0a1d67
|
1
|
| Totale |
15435 |
Archivio Istituzionale della Ricerca