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Sobre

Sobre

Ricardo Morla é professor auxiliar na Universidade do Porto. Ensina e desenvolve investigação no Departamento de Engenharia Electrotécnica e de Computadores da FEUP e no INESC TEC. Os seus interesses de investigação centram-se na gestão e no controlo de redes e sistemas IT. Aplica técnicas de análise de dados e técnicas de coordenação em grande escala para ajudar a gerir redes empresariais, serviços e infraestrutura IT, e sistemas de inteligência ambiente. É doutorado em Computação pela Universidade de Lancaster. Foi lecturer e post-doc na Universidade da Califórnia em Irvine em 2007, e professor convidado na Universidade de Carnegie Mellon em 2010 no âmbito do programa CMU-Portugal. Dinamiza o laboratório de Redes e Serviços da FEUP.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Ricardo Morla
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    15 setembro 1998
010
Publicações

2020

Does domain name encryption increase users' privacy?

Autores
Trevisan, M; Soro, F; Mellia, M; Drago, I; Morla, R;

Publicação
ACM SIGCOMM Computer Communication Review

Abstract

2020

Flow-based detection and proxy-based evasion of encrypted malware C2 traffic

Autores
Novo, C; Morla, R;

Publicação
CoRR

Abstract

2020

802.11 wireless simulation and anomaly detection using HMM and UBM

Autores
Allahdadi, A; Morla, R; Cardoso, JS;

Publicação
SIMULATION

Abstract
Despite the growing popularity of 802.11 wireless networks, users often suffer from connectivity problems and performance issues due to unstable radio conditions and dynamic user behavior, among other reasons. Anomaly detection and distinction are in the thick of major challenges that network managers encounter. The difficulty of monitoring broad and complex Wireless Local Area Networks, that often requires heavy instrumentation of the user devices, makes anomaly detection analysis even harder. In this paper we exploit 802.11 access point usage data and propose an anomaly detection technique based on Hidden Markov Model (HMM) and Universal Background Model (UBM) on data that is inexpensive to obtain. We then generate a number of network anomalous scenarios in OMNeT++/INET network simulator and compare the detection outcomes with those in baseline approaches—RawData and Principal Component Analysis. The experimental results show the superiority of HMM and HMM-UBM models in detection precision and sensitivity.

2019

Anomaly Detection and Modeling in 802.11 Wireless Networks

Autores
Allahdadi, A; Morla, R;

Publicação
CoRR

Abstract

2019

Predicting throughput in IEEE 802.11 based wireless networks using directional antenna

Autores
Kandasamy, S; Morla, R; Ramos, P; Ricardo, M;

Publicação
Wireless Networks

Abstract
In IEEE 802.11 based wireless networks interference increases as more access points are added. A metric helping to quantize this interference seems to be of high interest. In this paper we study the relationship between the (Formula presented.) metric, which captures interference, and throughput for IEEE 802.11 based network using directional antenna. The (Formula presented.) model was found to best represent the relationship between the interference metric and the network throughput. We use this model to predict the performance of similar networks and decide the best configuration a network operator could use for planning his network. © 2017 Springer Science+Business Media, LLC

Teses
supervisionadas

2020

Detection of Encrypted Malware Command and Control Traffic

Autor
Carlos António de Sousa Costa Novo

Instituição
UP-FCUP

2020

Performance Anomaly Detection in 802.11 Wireless Networks Applying Hidden Markov Models

Autor
Anisa Allahdadidastjerdi

Instituição
UP-FCUP

2020

A Two Stage Classifier for DGA Detection

Autor
Joaquim Pedro Marques Coelho dos Santos

Instituição
UP-FEUP

2020

Adversarial Malware Command and Control Traffic Generation

Autor
Carlos António de Sousa Costa Novo

Instituição
UP-FEUP

2020

Attacking an Autonomous Vehicle Brake Anomaly Detector with Adversarial Learning Techniques

Autor
Francisco Maria Fernandes Machado Santos

Instituição
UP-FEUP