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- Wissenschaftlicher Artikel (5) (entfernen)
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.
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.
Third-party tracking is a common and broadly used technique on the Web. Different defense mechanisms have emerged to counter these practices (e.g. browser vendors that ban all third-party cookies). However, these countermeasures only target third-party trackers and ignore the first party because the narrative is that such monitoring is mostly used to improve the utilized service (e.g. analytical services). In this paper, we present a large-scale measurement study that analyzes tracking performed by the first party but utilized by a third party to circumvent standard tracking preventing techniques. We visit the top 15,000 websites to analyze first-party cookies used to track users and a technique called “DNS CNAME cloaking”, which can be used by a third party to place first-party cookies. Using this data, we show that 76% of sites effectively utilize such tracking techniques. In a long-running analysis, we show that the usage of such cookies increased by more than 50% over 2021.
Software updates take an essential role in keeping IT environments secure. If service providers delay or do not install updates, it can cause unwanted security implications for their environments. This paper conducts a large-scale measurement study of the update behavior of websites and their utilized software stacks. Across 18 months, we analyze over 5.6M websites and 246 distinct client- and server-side software distributions. We found that almost all analyzed sites use outdated software. To understand the possible security implications of outdated software, we analyze the potential vulnerabilities that affect the utilized software. We show that software components are getting older and more vulnerable because they are not updated. We find that 95 % of the analyzed websites use at least one product for which a vulnerability existed.