Applied Cryptography and Network Security Workshops [electronic resource] : ACNS 2019 Satellite Workshops, SiMLA, Cloud S&P, AIBlock, and AIoTS, Bogota, Colombia, June 5–7, 2019, Proceedings /

SiMLA - Security in Machine Learning and its Applications -- Risk-based Static Authentication in Web Applications with Behavioral Biometrics and Session Context Analytics -- Using Honeypots in a Decentralized Framework to Defend Against Adversarial Machine-Learning Attacks -- Cloud S&P - Cloud Security and Privacy -- Graphene: A Secure Cloud Communication Architecture -- A Survey on Machine Learning applications for Software Defined Network Security -- AIBlock - Application Intelligence and Blockchain Security -- A New Proof of Work for Blockchain Based on Random Multivariate Quadratic Equations -- BSIEM-IoT: A Blockchain-based and Distributed SIEM for the Internet of Things -- Towards Blockchained Challenge-based Collaborative Intrusion Detection -- AIoTS - Artificial Intelligence and Industrial Internet-of-Things Security -- Enhancement to the Privacy-aware Authentication for Wi-Fi based Indoor Positioning Systems -- Design of a FDIA Resilient Protection Scheme for Power Networks by Securing Minimal Sensor Set -- Strong Leakage Resilient Encryption by Hiding Partial Ciphertext.

This book constitutes the proceedings of the satellite workshops held around the 17th International Conference on Applied Cryptography and Network Security, ACNS 2019, in Bogota, Colombia, in June 2019. The 10 papers presented in this volume were carefully reviewed and selected from 30 submissions. They stem from the following workshops: AIBlock 2019: First International Workshop on Application Intelligence and Blockchain Security AIoTS 2019: First International Workshop on Articial Intelligence and Industrial Internet-of-Things Security Cloud S&P 2019: First International Workshop on Cloud Security and Privacy PriDA 2019: First InternationalWorkshop on Privacy-preserving Distributed Data Analysis SiMLA 2019: First International Workshop on Security in Machine Learning and its Applications.