The seamless and secure integration of subsystems is a pivotal requirement within contemporary automotive development, necessitating the application of design methodologies like the Vee model. While this approach includes dedicated verification steps for the included contexts and provides a high level of assurance that the system will operate correctly under specified conditions, formalizing specifications outside its operational design domain is per definition not included. Additionally, black-box systems like machine learning based functions prove difficulty to test by these traditional methodologies. In this project, we introduce and demonstrate a design workflow combining the Vee model design paradigm with continuous data-driven software engineering. Our workflow assists the continuous, safe and secure development and improvement of consumer vehicle functionality over the product lifecycle. This is achieved through the continuous monitoring of anomalies, as well as system states that deviate from the established design domain. The UNCOVER methodology consists of a continuous reduction in the amount of necessary monitored messages and presents a methodology throughout the entirety of the product lifecycle. We demonstrate our methodology through a simulation and show our automatic generation of monitoring components, and an automated preselection of identified safety or security incidents.

In forensic computing, especially in the field of postmortem file system forensics, the reconstruction of lost or deleted files plays a major role. The techniques that can be applied to this end strongly depend on the specifics of the file system in question. Various file systems are already well-investigated, such as FAT16/32, NTFS for Microsoft Windows systems and Ext2/3/4 as the most common Linux file systems and HFS/HFS+ for macOS. There also exist tools, such as the famous Sleuthkit by Brian Carrier that provide file recovery features for those file systems by interpreting the file system's internal data structures. APFS is the new file system for Apple devices that is applied by default on all current iOS mobile devices, as well as macOS since High Sierra, and is thus currently rolled out on a large number of devices. However, for APFS, no forensic file recovery methodologies have been developed so far. In this paper, we propose different approaches to identify and recover (deleted) files on an APFS file system. We implemented our approaches as a proof of concept tool and evaluate those against each other and against file carving.

The JavaScript language is a core component of active and dynamic web content in the Internet today. Besides its great success in enhancing web applications, however, JavaScript provides the basis for so-called drive-by downloads---attacks exploiting vulnerabilities in web browsers and their extensions for unnoticeably downloading malicious software. Due to the diversity and frequent use of obfuscation in these attacks, static code analysis is largely ineffective in practice. While dynamic analysis and honeypots provide means to identify drive-by-download attacks, current approaches induce a significant overhead which renders immediate prevention of attacks intractable.