Formal Modeling and Verification for Domain Validation and ACME

Web traffic encryption has shifted from applying only to highly sensitive websites (such as banks) to a majority of all Web requests. Until recently, one of the main limiting factors for enabling HTTPS is the requirement to obtain a valid certificate from a trusted certification authority, a tedious process that typically involves fees and ad-hoc key generation, certificate request and domain validation procedures. To remove this barrier of entry, the Internet Security Research Group created Let's Encrypt, a new non-profit certificate authority which uses a new protocol called Automatic Certificate Management Environment (ACME) to automate certificate management at all levels (request, validation , issuance, renewal, and revocation) between clients (website operators) and servers (certificate authority nodes). Let's Encrypt's success is measured by its issuance of over 12 million free certificates since its launch in April 2016. In this paper, we survey the existing process for issuing domain-validated certificates in major certification authorities to build a security model of domain-validated certificate issuance. We then model the ACME protocol in the applied pi-calculus and verify its stated security goals against our threat model of domain validation. We compare the effective security of different domain validation methods and show that ACME can be secure under a stronger threat model than that of traditional CAs. We also uncover weaknesses in some flows of ACME 1.0 and propose verified improvements that have been adopted in the latest protocol draft submitted to the IETF

Formalizing and safeguarding blockchain-based BlockVoke protocol as an ACME extension for fast certificate revocation

Certificates are integral to the security of today’s Internet. Protocols like BlockVoke allow secure, timely and efficient revocation of certificates that need to be invalidated. ACME, a scheme used by the non-profit Let’s Encrypt Certificate Authority to handle most parts of the certificate lifecycle, allows automatic and seamless certificate issuance. In this work, we bring together both protocols by describing and formalizing an extension of the ACME protocol to support BlockVoke, combining the benefits of ACME’s certificate lifecycle management and BlockVoke’s timely and secure revocations. We then formally verify this extension through formal methods such as Colored Petri Nets (CPNs) and conduct a risk and threat analysis of the ACME/BlockVoke extension using the ISSRM domain model. Identified risks and threats are mitigated to secure our novel extension. Furthermore, a proof-of-concept implementation of the ACME/BlockVoke extension is provided, bridging the gap towards deployment in the real world

SAVCBS 2003: Specification and Verification of Component-Based Systems

These are the proceedings for the SAVCBS 2003 workshop. This workshop was held at ESEC/FSE 2003 in Helsinki Finland in September 2003

Software Engineering for Science

Software Engineering for Science provides an in-depth collection of peer-reviewed chapters that describe experiences with applying software engineering practices to the development of scientific software. It provides a better understanding of how software engineering is and should be practiced, and which software engineering practices are effective for scientific software. The book starts with a detailed overview of the Scientific Software Lifecycle, and a general overview of the scientific software development process. It highlights key issues commonly arising during scientific software development, as well as solutions to these problems. The second part of the book provides examples of the use of testing in scientific software development, including key issues and challenges. The chapters then describe solutions and case studies aimed at applying testing to scientific software development efforts. The final part of the book provides examples of applying software engineering techniques to scientific software, including not only computational modeling, but also software for data management and analysis. The authors describe their experiences and lessons learned from developing complex scientific software in different domains