Web measurement studies can shed light on not yet fully understood phenomena and thus are essential for analyzing how the modern Web works. This often requires building new and adjustinng existing crawling setups, which has led to a wide variety of analysis tools for different (but related) aspects. If these efforts are not sufficiently documented, the reproducibility and replicability of the measurements may suffer—two properties that are crucial to sustainable research. In this paper, we survey 117 recent research papers to derive best practices for Web-based measurement studies and specify criteria that need to be met in practice. When applying these criteria to the surveyed papers, we find that the experimental setup and other aspects essential to reproducing and replicating results are often missing. We underline the criticality of this finding by performing a large-scale Web measurement study on 4.5 million pages with 24 different measurement setups to demonstrate the influence of the individual criteria. Our experiments show that slight differences in the experimental setup directly affect the overall results and must be documented accurately and carefully.
Künstliche Intelligenz (KI) ermöglicht es, komplexe Zusammenhänge und Muster aus großen Datenmengen zu extrahieren und in einem statistischen Modell zu erfassen. Dieses KI-Modell kann anschließend Aussagen über zukünftig auftretende Daten treffen. Mit dem zunehmenden Einsatz von Künstlicher Intelligenz rücken solche Systeme auch immer mehr ins Visier von Cyberkriminellen. Der Artikel beschreibt umfassend Angriffsszenarien und mögliche Abwehrmaßnahmen.