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Publications

Publications by CRACS

2016

Android: desenvolvimento de aplicações com Android Studio

Authors
Queirós, Ricardo;

Publication

Abstract
O tema do livro é o desenvolvimento de aplicações para Android 6 usando o novo Android Studio e abordando tópicos emergentes como o novo paradigma de desenho Material Design e uma introdução à plataforma Android Wear. O livro aborda os seguintes temas: - Introdução ao Android e ao Android Studio - Interface Gráfica e Material Design - Gestão de Dados, Multimédia e Networking - Mapas e Localização - Introdução ao Android Wear

2016

Proceedings of the Eleventh Workshop on Logical Frameworks and Meta-Languages: Theory and Practice, LFMTP 2016, Porto, Portugal, June 23, 2016

Authors
Dowek, G; Licata, DR; Alves, S;

Publication
LFMTP

Abstract

2016

Report on FSCD 2016: 1st International Conference on Formal Structures for Computation and Deduction

Authors
Alves, S;

Publication
ACM SIGLOG News

Abstract

2016

The G-ACM Tool: using the Drools Rule Engine for Access Control Management

Authors
Sá, J; Alves, S; Broda, S;

Publication
CoRR

Abstract

2016

Geometry-Based Propagation Modeling and Simulation of Vehicle-to-Infrastructure Links

Authors
Aygun, B; Boban, M; Vilela, JP; Wyglinski, AM;

Publication
2016 IEEE 83RD VEHICULAR TECHNOLOGY CONFERENCE (VTC SPRING)

Abstract
Due to the differences in terms of antenna height, scatterer density, and relative speed, V2I links exhibit different propagation characteristics compared to V2V links. We develop a geometry-based path loss and shadow fading model for V2I links. We separately model the following types of V2I links: line-of-sight, non-line-of-sight due to vehicles, non-line-of-sight due to foliage, and non-line-of-sight due to buildings. We validate the proposed model using V2I field measurements. We implement the model in the GEMV2 simulator, and make the source code publicly available.

2016

Expedite Feature Extraction for Enhanced Cloud Anomaly Detection

Authors
Dalmazo, BL; Vilela, JP; Simoes, P; Curado, M;

Publication
NOMS 2016 - 2016 IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM

Abstract
Cloud computing is the latest trend in business for providing software, platforms and services over the Internet. However, a widespread adoption of this paradigm has been hampered by the lack of security mechanisms. In view of this, the aim of this work is to propose a new approach for detecting anomalies in cloud network traffic. The anomaly detection mechanism works on the basis of a Support Vector Machine (SVM). The key requirement for improving the accuracy of the SVM model, in the context of cloud, is to reduce the total amount of data. In light of this, we put forward the Poisson Moving Average predictor which is the core of the feature extraction approach and is able to handle the vast amount of information generated over time. In addition, two case studies are employed to validate the effectiveness of the mechanism on the basis of real datasets. Compared with other approaches, our solution exhibits the best performance in terms of detection and false alarm rates.

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