Demo Data from EDBT 2026 / reviewers in the wild / expert
Francisco Chamiça Pereira
dblp:356/8106 · also Francisco Pereira 0004
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2024
0000-0002-6215-3569ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 2 · 1 first-author · 2 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
2 papers |
Software-defined and programmable networks · 100% | |
| Network and information security
1 paper |
Network security · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Parallel and multicore computing · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software-defined and programmable networks
software network functions |
0.8 | 1 | 2024 | Automatic Parallelization of Software Network Functions · NSDI 2024 |
Parallel and multicore computing › parallel programming models
automatic parallelization |
0.8 | 1 | 2024 | Automatic Parallelization of Software Network Functions · NSDI 2024 |
Software-defined and programmable networks › programmable data plane
in-network machine learning |
0.7 | 1 | 2023 | Poster: In-Network ML Feature Computation for Malicious Traffic Detection · SIGCOMM 2023 |
Software-defined and programmable networks
programmable data plane |
0.7 | 1 | 2023 | Poster: In-Network ML Feature Computation for Malicious Traffic Detection · SIGCOMM 2023 |
Network security › intrusion detection and prevention
intrusion detection |
0.7 | 1 | 2023 | Poster: In-Network ML Feature Computation for Malicious Traffic Detection · SIGCOMM 2023 |
Network security › intrusion detection and prevention › intrusion detection
malicious traffic detection |
0.7 | 1 | 2023 | Poster: In-Network ML Feature Computation for Malicious Traffic Detection · SIGCOMM 2023 |
Methods — techniques the papers use, named apart from their topics
machine learning feature computation · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Automatic Parallelization of Software Network Functions
Francisco Chamiça Pereira, Fernando M. V. Ramos, Luis Pedrosa |
NSDI | 1 |
| 2023 | Poster: In-Network ML Feature Computation for Malicious Traffic DetectionabstractWe present Peregrine, a malicious traffic detector that offloads part of its computation to a programmable switch. The idea is to partition detection, by moving the ML feature computation module from a middlebox server to a switch data plane. The key innovation unlocked---computing the ML input features over all traffic---results in a significant improvement in detection performance: in our evaluation, up to 5.7x over the state of the art. João Romeiras Amado, Francisco Chamiça Pereira, Salvatore Signorello, Miguel Correia 0001, Fernando M. V. Ramos |
SIGCOMM | 2 |