Innovation in the teaching of sustainable development in Europe: the case of ISLE Erasmus Network

Sustainable Development (SD) is one of the most widely used terms during the last years. It is a multidisciplinary concept, which applies mostly to life sciences but is not limited to them. Even though the short survey conducted by the authors revealed that there are only a few cases of Higher Educational Institutes (HEIs) around Europe that provide programs dedicated to SD, it is obvious that there is a constant raise in the need for implementing courses related to SD in existing programs. This paper discusses the case study of I.S.L.E., an Erasmus Academic Network, which aims to use the existing knowledge and tools in the context of teaching sustainable development topics in Universities and HEIs around Europe as a basis, and elaborate further by introducing an innovative approach towards the improvement of teaching SD in HEIs, based on the current needs as they are identified by the actions of the Network.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.