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- Cybersecurity and Digital Forensics – Course Development in a Higher Education InstitutionPublication . Antunes, Mário; Rabadão, CarlosIndividuals and companies have a feeling of insecurity in the Internet, as every day a reasonable amount of attacks take place against users’ privacy and confidentiality. The use of digital equipment in illicit and unlawful activities has increasing. Attorneys, criminal polices, layers and courts staff have to deal with crimes committed with digital “weapons”, whose evidences have to be examined and reported by applying digital forensics methods. Digital forensics is a recent and fast-growing area of study which needs more graduated professionals. This fact has leveraged higher education institutions to develop courses and curricula to accommodate digital forensics topics and skills in their curricular offers. This paper aims to present the development of a cybersecurity and digital forensics master course in Polytechnic of Leiria, a public higher education institution in Portugal. The authors depict the roadmap and the general milestones that lead to the development of the course. The strengths and opportunities are identified and the major students’ outcomes are pointed out. The way taken and the decisions made are also approached, with a view to understanding the performance obtained so far.
- A Hybrid AIS-SVM Ensemble Approach for Text ClassificationPublication . Antunes, Mário; Silva, Catarina; Ribeiro, Bernardete; Correia, ManuelIn this paper we propose and analyse methods for expanding state-of-the-art performance on text classification. We put forward an ensemble-based structure that includes Support Vector Machines (SVM) and Artificial Immune Systems (AIS). The underpinning idea is that SVM-like approaches can be enhanced with AIS approaches which can capture dynamics in models. While having radically different genesis, and probably because of that, SVM and AIS can cooperate in a committee setting, using a heterogeneous ensemble to improve overall performance, including a confidence on each system classification as the differentiating factor. Results on the well-known Reuters-21578 benchmark are presented, showing promising classification performance gains, resulting in a classification that improves upon all baseline contributors of the ensemble committee.