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About

Adjunt professor at Computers Engineering Department, ESTG-Leiria (Polytechnic of Leiria) and reseracher at CRACS.

Holds a PHD in Computer Science by Universidade do Porto; MSc in Informatics, branch of systems and networks, also by Universidade do Porto; Degree in Computers Enginnering by Instituto Superior de Engenharia do Porto (Polytechnic of Porto).

Coordinates a MSc course in cybersecurity and digital forensics at Polytechnic of Leiria and is responsible by classes on networking, systems administration, cloud technology, networking security and datacenters infrastrucutres.

Main areas of research include immune-inspired algorithms applied to automatic detection of anomalies, ensemble based algorithms for classification and anomaly detection, learning on dynamic systems in a temporal basis.

Previously he was algo ICT project manager and system administrator in companies.

Interest
Topics
Details

Details

  • Name

    Mário João Antunes
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2009
Publications

2021

Evaluating cybersecurity attitudes and behaviors in Portuguese healthcare institutions

Authors
Nunes, P; Antunes, M; Silva, C;

Publication
Procedia Computer Science

Abstract

2021

Information Security and Cybersecurity Management: A Case Study with SMEs in Portugal

Authors
Antunes, M; Maximiano, M; Gomes, R; Pinto, D;

Publication
Journal of Cybersecurity and Privacy

Abstract
Information security plays a key role in enterprises management, as it deals with the confidentiality, privacy, integrity, and availability of one of their most valuable resources: data and information. Small and Medium-sized enterprises (SME) are seen as a blind spot in information security and cybersecurity management, which is mainly due to their size, regional and familiar scope, and financial resources. This paper presents an information security and cybersecurity management project, in which a methodology based on the well-known ISO-27001:2013 standard was designed and implemented in fifty SMEs that were located in the center region of Portugal. The project was conducted by a business association located at the center of Portugal and mainly participated by SMEs. The Polytechnic of Leiria and an IT auditing/consulting team were the other two entities that participated on the project. The characterisation of the participating enterprises, the ISO-27001:2013 based methodology developed and implemented in SMEs, as well as the results obtained in this case study, are depicted and analysed in the paper. The attained results show a clear benefit to the audited and intervened SMEs, being mainly attested by the increasing of their information security management robustness and collaborators’ cyberawareness.

2020

Boosting dynamic ensemble’s performance in Twitter

Authors
Costa, J; Silva, C; Antunes, M; Ribeiro, B;

Publication
Neural Computing and Applications

Abstract
Many text classification problems in social networks, and other contexts, are also dynamic problems, where concepts drift through time, and meaningful labels are dynamic. In Twitter-based applications in particular, ensembles are often applied to problems that fit this description, for example sentiment analysis or adapting to drifting circumstances. While it can be straightforward to request different classifiers' input on such ensembles, our goal is to boost dynamic ensembles by combining performance metrics as efficiently as possible. We present a twofold performance-based framework to classify incoming tweets based on recent tweets. On the one hand, individual ensemble classifiers' performance is paramount in defining their contribution to the ensemble. On the other hand, examples are actively selected based on their ability to effectively contribute to the performance in classifying drifting concepts. The main step of the algorithm uses different performance metrics to determine both each classifier strength in the ensemble and each example importance, and hence lifetime, in the learning process. We demonstrate, on a drifted benchmark dataset, that our framework drives the classification performance considerably up for it to make a difference in a variety of applications. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

2020

Boosting dynamic ensemble's performance in Twitter

Authors
Cósta, J; Silva, C; Antunes, M; Ribeiro, B;

Publication
Neural Computing and Applications

Abstract

2020

Benchmarking Behavior-Based Intrusion Detection Systems with Bio-inspired Algorithms

Authors
Ferreira, P; Antunes, M;

Publication
Security in Computing and Communications - 8th International Symposium, SSCC 2020, Chennai, India, October 14-17, 2020, Revised Selected Papers

Abstract

Supervised
thesis

2017

Uma implementação open source de um serviço de cloud do tipo IaaS

Author
João Vitoria Santos

Institution
IPLeiria

2017

Using telemedicine WebRTC tests in hospital environment

Author
Dário Gabriel da Cruz Santos

Institution
IPLeiria