Demo Data from EDBT 2026 / reviewers in the wild / expert
Victor Aguiar Coutinho
dblp:394/2261
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Cellular and mobile networks · 61% Software-defined and programmable networks · 30% Network measurement and analytics · 9% | |
| Network and information security
1 paper |
Network security · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software-defined and programmable networks › SDN threat detection
DDoS detection |
0.8 | 1 | 2024 | Evaluation of the Latency of Machine Learning Random Access DDoS Detection in Open RAN · MobiCom 2024 |
Cellular and mobile networks › radio access networks
Open RAN |
0.8 | 1 | 2024 | Evaluation of the Latency of Machine Learning Random Access DDoS Detection in Open RAN · MobiCom 2024 |
Cellular and mobile networks
radio access networks |
0.8 | 1 | 2024 | Evaluation of the Latency of Machine Learning Random Access DDoS Detection in Open RAN · MobiCom 2024 |
Network security › intrusion detection and prevention
intrusion detection |
0.8 | 1 | 2024 | Evaluation of the Latency of Machine Learning Random Access DDoS Detection in Open RAN · MobiCom 2024 |
Network measurement and analytics
latency measurement |
0.2 | 1 | 2024 | Evaluation of the Latency of Machine Learning Random Access DDoS Detection in Open RAN · MobiCom 2024 |
Methods — techniques the papers use, named apart from their topics
machine learning · 1.5XGBoost · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Evaluation of the Latency of Machine Learning Random Access DDoS Detection in Open RANabstractA testbed with open-source tools and commercial equipment is designed to investigate the timing of ML-based xApp operations in DDoS attack detection during the Random Access procedure. Results show high-precision and high-recall ML classifiers operating faster than the Medium Access Control (MAC) layer's Contention Resolution timer. For classifiers with over 97% precision and recall, XGBoost has the fastest execution time and the narrowest estimated run time probability density function. Paulo Ricardo Branco da Silva, João Paulo S. H. Lima, Erika Costa Alves, William Sanchez Farfan, Victor Aguiar Coutinho, Thomas William do Prado Paiva, Daniel Lazkani Feferman, Francisco Hugo Costa Neto |
MobiCom | 5 |