Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018) This book highlights recent research on Soft Computing Pattern Recognition Information Assurance and Security. It presents 38 selected papers from the 10th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018) and the 14th International Conference on Information Assurance and Security (IAS 2018) held at Instituto Superior de Engenharia do Porto (ISEP) Portugal during December 13–15 2018. SoCPaR – IAS 2018 is a premier conference and brings together researchers engineers and practitioners whose work involves soft computing and information assurance and their applications in industry and the real world. Including contributions by authors from over 25 countries the book offers a valuable reference guide for all researchers students and practitioners in the fields of Computer Science and Engineering An Efficient and Secure Forward Error Correcting Scheme for DNA Data Storage A Blockchain based Scheme for Access Control in e Health Scenarios Blockchain based PKI for Crowdsourced IoT Sensor Information The Design of a Cloud Forensics Middleware System Base on Memory Analysis Privacy Enhancement of Telecom Processes Interacting with Charging Data Records Warning of Aﬀected Users About an Identity Leak Network Security Evaluation and Training Based on Real World Scenarios of Vulnerabilities Detected in Portuguese Municipalities’ Network Devices A Novel Concept of Firewall Filtering Service Based on Rules Trust Risk Assessment A survey of blockchain frameworks and applications Filtering Email Addresses Credit Card Numbers and searching for Bitcoin Artifacts with the Autopsy Digital Forensics Software A survey on the use of data points in IDS research Cybersecurity and digital forensics – course development in a higher education institution Model Driven Architectural Design of Information Security System An Automated System for Criminal Police Reports Analysis Detecting Internet Scale Traﬃc Redirection Attacks using Latent Class Models Passive Video Forgery Detection Considering Spatio Temporal Consistency