Architectural Data Flow Analysis For Detecting Violations Of Confidentiality Requirements

Download Architectural Data Flow Analysis For Detecting Violations Of Confidentiality Requirements PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Architectural Data Flow Analysis For Detecting Violations Of Confidentiality Requirements book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Architectural Data Flow Analysis for Detecting Violations of Confidentiality Requirements

Author: Seifermann, Stephan
language: en
Publisher: KIT Scientific Publishing
Release Date: 2022-12-09
Software vendors must consider confidentiality especially while creating software architectures because decisions made here are hard to change later. Our approach represents and analyzes data flows in software architectures. Systems specify data flows and confidentiality requirements specify limitations of data flows. Software architects use detected violations of these limitations to improve the system. We demonstrate how to integrate our approach into existing development processes.
Architecture-based Evolution of Dependable Software-intensive Systems

Author: Heinrich, Robert
language: en
Publisher: KIT Scientific Publishing
Release Date: 2023-06-05
This cumulative habilitation thesis, proposes concepts for (i) modelling and analysing dependability based on architectural models of software-intensive systems early in development, (ii) decomposition and composition of modelling languages and analysis techniques to enable more flexibility in evolution, and (iii) bridging the divergent levels of abstraction between data of the operation phase, architectural models and source code of the development phase.
Evaluating Architectural Safeguards for Uncertain AI Black-Box Components

Author: Scheerer, Max
language: en
Publisher: KIT Scientific Publishing
Release Date: 2023-10-23
Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability.