CIIC - Publicações em Atas de Conferências com Peer Review
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Percorrer CIIC - Publicações em Atas de Conferências com Peer Review por autor "Antunes, Mário"
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- Benchmarking bioinspired machine learning algorithms with CSE-CIC-IDS2018 network intrusions datasetPublication . Ferreira, Paulo; Antunes, MárioThis paper aims to evaluate CSE-CIC-IDS2018 network intrusions dataset and benchmark a set of supervised bioinspired machine learning algo rithms, namely CLONALG Artificial Immune System, Learning Vector Quantization (LVQ) and Back-Propagation Multi-Layer Perceptron (MLP). The results obtained were also compared with an ensemble strategy based on a majority voting algorithm. The results obtained show the appropri ateness of using the dataset to test behaviour based network intrusion de tection algorithms and the efficiency of MLP algorithm to detect zero-day attacks, when comparing with CLONALG and LVQ.
- Evaluating cybersecurity attitudes and behaviors in Portuguese healthcare institutionsPublication . Nunes, Paulo; Antunes, Mário; Silva, CarinaThe growing digitization of healthcare institutions and its increasing dependence on Internet infrastructure has boosted the concerns related to data privacy and confidentiality. These institutions have been challenged with specific issues, namely the sensitivity of data, the specificity of networked equipment, the heterogeneity of healthcare professionals (nurses, doctors, administrative staff and other) and the IT skills they have.
- Identification of Fake Profiles in Twitter Social NetworkPublication . Antunes, Mário; Baptista, Hugo; Rodrigues, BaltazarOnline social networks are being intensively used by millions of users, Twitter being one of the most popular, as a powerful source of information with impact on opinion and decision making. However, in Twitter as in other online social networks, not all the users are legitimate, and it is not easy to detect those accounts that correspond to fake profiles. In this work in progress paper, we propose a method to help practitioners to identify fake Twitter accounts, by calculating the “fake probability” based on a weighted parameter set collected from public Twitter accounts. The preliminary results obtained with a subset of an existing annotated dataset of Twitter accounts are promising and give confidence on using this method as a decision support system, to help practitioners to identify fake profiles.
- On using crowdsourcing and active learning to improve classification performancePublication . Costa, Joana; Silva, Catarina; Antunes, Mário; Ribeiro, BernardeteCrowdsourcing is an emergent trend for general-purpose classification problem solving. Over the past decade, this notion has been embodied by enlisting a crowd of humans to help solve problems. There are a growing number of real-world problems that take advantage of this technique, such as Wikipedia, Linux or Amazon Mechanical Turk. In this paper, we evaluate its suitability for classification, namely if it can outperform state-of-the-art models by combining it with active learning techniques. We propose two approaches based on crowdsourcing and active learning and empirically evaluate the performance of a baseline Support Vector Machine when active learning examples are chosen and made available for classification to a crowd in a web-based scenario. The proposed crowdsourcing active learning approach was tested with Jester data set, a text humour classification benchmark, resulting in promising improvements over baseline results.