This book constitutes the revised selected papers from the 13th International Conference on Risks and Security of Internet and Systems, CRiSIS 2018, held in Arcachon, France, in October 2018.

The 12 full papers and 6 short papers presented in this volume were carefully reviewed and selected from 34 submissions. They cover diverse research themes that range from classic topics, such as vulnerability analysis and classification; apps security; access control and filtering; cloud security; cyber-insurance and cyber threat intelligence; human-centric security and trust; and risk analysis.

This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of  the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers  a comprehensive preamble for further  problem–oriented chapters. 

The most interesting and open problems of machine learning in the framework of  Deep Learning are discussed in this book and solutions are proposed.  This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks.  This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. 

Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.

This book constitutes the revised selected papers from the 13th International Conference on Risks and Security of Internet and Systems, CRiSIS 2018, held in Arcachon, France, in October 2018.

The 12 full papers and 6 short papers presented in this volume were carefully reviewed and selected from 34 submissions. They cover diverse research themes that range from classic topics, such as vulnerability analysis and classification; apps security; access control and filtering; cloud security; cyber-insurance and cyber threat intelligence; human-centric security and trust; and risk analysis.

This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of  the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers  a comprehensive preamble for further  problem–oriented chapters. 

The most interesting and open problems of machine learning in the framework of  Deep Learning are discussed in this book and solutions are proposed.  This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks.  This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. 

Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.