Joao Romeiras Amado
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Joao Romeiras Amado has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Fernando Manuel Valente Ramos
LaSIGE, Faculdade de Ciências, Universidade de Lisboa, Portugal
2 shared publications - Miguel Correia
Universidade de Lisboa
2 shared publications - Salvatore Signorello
NOVA University Lisbon, Lisbon, Portugal
2 shared publications - Francisco Chamiça Pereira
INESC-ID, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
1 shared publication
Latest Publications
- Open Access
- 10 September 2023
- Published by Association for Computing Machinery (ACM)
- p. 1105-1107
Poster: In-Network ML Feature Computation for Malicious Traffic Detection
- 10 September 2023
- Published by Association for Computing Machinery (ACM)
- p. 1105-1107
Open AccessPoster: In-Network ML Feature Computation for Malicious Traffic Detection
- 13 October 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-2
Poster: Speeding Up Network Intrusion Detection
Modern network data planes have enabled new measurement approaches, including efficient sketch-based techniques with provable trade-offs between memory and accuracy, directly in the data plane, at line rate. We thus ask the question: can one leverage this richer measurement plane to improve network intrusion detection? Our answer is SPID, a push-based, feature-rich network monitoring approach to assist learning-based attack detection. SPID switches run a diverse set of measurement primitives and proactively push measurements to the monitoring system when relevant changes occur. Network measurements are then fed as input features to a classifier based on unsupervised learning to detect ongoing attacks, as they occur. In consequence, SPID aims to reduce attack detection time, when comparing to existing solutions present in large scale networks.
- 13 October 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-2
Poster: Speeding Up Network Intrusion Detection
Modern network data planes have enabled new measurement approaches, including efficient sketch-based techniques with provable trade-offs between memory and accuracy, directly in the data plane, at line rate. We thus ask the question: can one leverage this richer measurement plane to improve network intrusion detection? Our answer is SPID, a push-based, feature-rich network monitoring approach to assist learning-based attack detection. SPID switches run a diverse set of measurement primitives and proactively push measurements to the monitoring system when relevant changes occur. Network measurements are then fed as input features to a classifier based on unsupervised learning to detect ongoing attacks, as they occur. In consequence, SPID aims to reduce attack detection time, when comparing to existing solutions present in large scale networks.