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About

About

In 2009 I received M.SC degree in Network and Information Systems Engineering from Faculty of Sciences of the University of Porto. Since 2009 I am a researcher in Center for Research in Advanced Computing Systems (CRACS-INESC TEC). My research interest include entity identification, data disambiguation, matching algorithms, data mining, information retrieval, web technologies and web design. Currently I work on project Authenticus, software platform that automatically associates publication authors to known researchers and corresponding publication records to Portuguese institutions.

Interest
Topics
Details

Details

003
Publications

2023

On the Performance of Secure Sharing of Classified Threat Intelligence between Multiple Entities

Authors
Fernandes, R; Bugla, S; Pinto, P; Pinto, A;

Publication
SENSORS

Abstract
The sharing of cyberthreat information within a community or group of entities is possible due to solutions such as the Malware Information Sharing Platform (MISP). However, the MISP was considered limited if its information was deemed as classified or shared only for a given period of time. A solution using searchable encryption techniques that better control the sharing of information was previously proposed by the same authors. This paper describes a prototype implementation for two key functionalities of the previous solution, considering multiple entities sharing information with each other: the symmetric key generation of a sharing group and the functionality to update a shared index. Moreover, these functionalities are evaluated regarding their performance, and enhancements are proposed to improve the performance of the implementation regarding its execution time. As the main result, the duration of the update process was shortened from around 2922 s to around 302 s, when considering a shared index with 100,000 elements. From the security analysis performed, the implementation can be considered secure, thus confirming the secrecy of the exchanged nonces. The limitations of the current implementation are depicted, and future work is pointed out.

2023

Produção Científica do Instituto Politécnico do Porto 2007-2021 - Web of Science

Authors
Elizabeth Sousa Vieira; Sylwia Bugla; Stella M. Abreu; Henri Nouws; Cristina Delerue Matos;

Publication

Abstract

2022

Inbreeding and research collaborations in Portuguese higher education

Authors
Tavares, O; Sin, C; Sa, C; Bugla, S; Amaral, A;

Publication
HIGHER EDUCATION QUARTERLY

Abstract
The aim of this paper is to analyse the relationship between academic inbreeding in Portugal and research collaboration, using co-authored publications as proxies. As previous research has shown that inbreeding is detrimental for research collaborations, it is hypothesised that academic inbreeding will lead to smaller research networks and, consequently, to fewer co-authored publications outside the institution of affiliation. Relying on a large data set which merged information on academics, their inbreeding status and their publications, binomial negative and fractional models were estimated to test the hypothesis. Findings show that inbred academics have smaller research networks; while they publish most co-authored papers, the relative weight of publications written in collaboration with institutional colleagues is the highest. In contrast, non-inbred academics with foreign PhDs have larger co-authorship networks. However, they publish most single-authored papers and the weight of their international co-authorships is heaviest.

2012

Comparison of co-authorship networks across scientific fields using motifs

Authors
Choobdar, S; Ribeiro, P; Bugla, S; Silva, F;

Publication
2012 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM)

Abstract
Comparing scientific production across different fields of knowledge is commonly controversial and subject to disagreement. Such comparisons are often based on quantitative indicators, such as papers per researcher, and data normalization is very difficult to accomplish. Different approaches can provide new insight and in this paper we focus on the comparison of different scientific fields based on their research collaboration networks. We use co-authorship networks where nodes are researchers and the edges show the existing co-authorship relations between them. Our comparison methodology is based on network motifs, which are over represented patterns, or subgraphs. We derive motif fingerprints for 22 scientific fields based on 29 different small motifs found in the corresponding co-authorship networks. These fingerprints provide a metric for assessing similarity among scientific fields, and our analysis shows that the discrimination power of the 29 motif types is not identical. We use a co-authorship dataset built from over 15,361 publications inducing a co-authorship network with over 32,842 researchers. Our results also show that we can group different fields according to their fingerprints, supporting the notion that some fields present higher similarity and can be more easily compared.