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About

About

Ricardo Morla is an assistant professor at the University of Porto. He teaches and does research at the Electrical and Computer Engineering Department at FEUP and at INESC TEC. His research interests are in management and control of IT systems and networks. He uses data analysis techniques and large-scale coordination techniques to help manage enterprise networks, IT services and infrastructure, and ambient intelligence systems. Ricardo holds a PhD in Computing from Lancaster University. He was a lecturer and post-doc at UC Irvine in 2007, and a visiting faculty at Carnegie Mellon University in 2010 under the CMU-Portugal program. He runs the Network and Services Laboratory at FEUP.

Interest
Topics
Details

Details

  • Name

    Ricardo Morla
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    15th September 1998
010
Publications

2020

Does domain name encryption increase users' privacy?

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

Publication
ACM SIGCOMM Computer Communication Review

Abstract

2020

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

Authors
Novo, C; Morla, R;

Publication
CoRR

Abstract

2020

802.11 wireless simulation and anomaly detection using HMM and UBM

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

Publication
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

Authors
Allahdadi, A; Morla, R;

Publication
CoRR

Abstract

2019

Predicting throughput in IEEE 802.11 based wireless networks using directional antenna

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

Publication
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

Supervised
thesis

2020

Attacking an Autonomous Vehicle Brake Anomaly Detector with Adversarial Learning Techniques

Author
Francisco Maria Fernandes Machado Santos

Institution
UP-FEUP

2020

Analysis of Intrusion Detection Log Data on a Scalable Environment

Author
Rui Miguel Almeida Oliveira

Institution
UP-FEUP

2020

Detection of Encrypted Malware Command and Control Traffic

Author
Carlos António de Sousa Costa Novo

Institution
UP-FCUP

2020

Performance Anomaly Detection in 802.11 Wireless Networks Applying Hidden Markov Models

Author
Anisa Allahdadidastjerdi

Institution
UP-FCUP

2020

A Two Stage Classifier for DGA Detection

Author
Joaquim Pedro Marques Coelho dos Santos

Institution
UP-FEUP