This book constitutes revised selected papers from the 4th International Workshop on Graphical Models for Security, GraMSec 2017, held in Santa Barbara, CA, USA, in August 2017. The 5 full and 4 short papers presented in this volume were carefully reviewed and selected from 19 submissions. The book also contains one invited paper from the WISER project. The contributions deal with the latest research and developments on graphical models for security.

This book constitutes the proceedings of the 7th International Workshop on Graphical Models for Security, GramSec 2020, which took place on June 22, 2020. The workshop was planned to take place in Boston, MA, USA but changed to a virtual format due to the COVID-19 pandemic. The 7 full and 3 short papers presented in this volume were carefully reviewed and selected from 14 submissions. The papers were organized in topical sections named: attack trees; attacks and risks modelling and visualization; and models for reasoning about security.

This book constitutes revised papers from the 6th International Workshop on Graphical Models for Security, GraMSec 2019, held in Hoboken, NJ, USA, in June 2019. The 8 full papers presented in this volume were carefully reviewed and selected from 15 submissions. The book also contains two invited talk. The contributions deal with the latest research and developments on graphical models for security.

This volume constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on Graphical Models for Security, GraMSec 2015, held in Verona, Italy, in July 2015.The 5 revised full papers presented together with one short tool paper and one invited lecture were carefully reviewed and selected from 13 submissions. The workshop contributes to the development of well-founded graphical security models, efficient algorithms for their analysis, as well as methodologies for their practical usage, thus providing an intuitive but systematic methodology to analyze security weaknesses of systems and to evaluate potential protection measures. /div

This book constitutes revised selected papers from the 5th International Workshop on Graphical Models for Security, GraMSec 2018, held in Oxford, UK, in July 2018. The 7 full papers presented in this volume were carefully reviewed and selected from 21 submissions. The book also contains one invited talk. The contributions deal with the latest research and developments on graphical models for security.

This book constitutes the refereed proceedings from the Third International Workshop on Graphical Models for Security, GraMSec 2016, held in Lisbon, Portugal, in June 2016. The 9 papers presented in this volume were carefully reviewed and selected from 23 submissions. The volume also contains the invited talk by Xinming Ou. GraMSec contributes to the development of well-founded graphical security models, efficient algorithms for their analysis, as well as methodologies for their practical usage.

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.