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Web advertisements are the primary financial source for many online services, but also for cybercriminals. Successful ad campaigns rely on good online profiles of their potential customers. The financial potentials of displaying ads have led to the rise of malware that injects or replaces ads on websites, in particular, so-called adware. This development leads to always further optimized and customized advertising. For these customization's, various tracking methods are used. However, only sparse work has gone into privacy issues emerging from adware. In this paper, we investigate the tracking capabilities and related privacy implications of adware and potentially unwanted programs (PUPs). Therefore, we developed a framework that allows us to analyze any network communication of the Firefox browser on the application level to circumvent encryption like TLS. We use this to dynamically analyze the communication streams of over 16,000 adware or potentially unwanted programs samples that tamper with the users' browser session. Our results indicate that roughly 37% of the requests issued by the analyzed samples contain private information and are accordingly able to track users. Additionally, we analyze which tracking techniques and services are used.
In diesem Artikel wird ein Alert-System für das Online-Banking vorgestellt, welches das Schutzniveau im Kontext von Social-Engineering-Angriffen sowohl clientseitig als auch serverseitig erhöhen soll. Hierfür wird durch das Alert-System ein kontinuierliches Lagebild über die aktuelle Gefahrenlage beim Online-Banking erstellt. Bei konkretem Bedarf wird der Nutzer punktuell vor aktuellen Betrugsmaschen gewarnt und zielgerichtet über Schutzvorkehrungen und Handlungsempfehlungen informiert. Für die Berechnung der aktuellen Gefahrenlage wurden unterschiedliche off-the-shelf-Algorithmen des Maschinellen Lernens verwendet und miteinander verglichen. Die Effektivität des Alert-Systems wurde anhand von echten Betrugsfällen evaluiert, die bei einer Bankengruppe in Deutschland aufgetreten sind. Zusätzlich wurde die Usability des Systems in einer Nutzerstudie mit 50 Teilnehmern untersucht. Die ersten Ergebnisse zeigen, dass die verwendeten Verfahren dazu geeignet sind, die Gefahrenlage im Online-Banking zu beurteilen und dass ein solches Alert-System auf hohe Akzeptanz bei Nutzern stößt.
In der modernen Informationsgesellschaft nehmen Online-Transaktionen einen wichtigen Teil unseres täglichen Lebens ein. In dieser Arbeit stellen wir ein nutzerzentriertes Protokoll vor, dass es Nutzern erlaubt vertrauenswürdige und sichere Transkationen durchzuführen selbst wenn sie ein nicht vertrau-enswürdiges oder mit Schadsoftware infiziertes Gerät nutzen. Das Protokoll nutzt einen CAPTCHA-artigen Ansatz, der verhindert, dass ein Angreifer eine Transaktion verändert ohne, dass Server oder Client dies bemerken. Dazu stellen wir dem Nutzer eine Aufgabe die kontextsensitive Informationen der Transaktion enthält. Die Aufgabe wird so gestellt, dass sie einfach von Menschen lösbar ist aber nur schwer automatisiert gelöst werden kann. Zur Evaluation des Systems haben wir eine Nutzerstudie (n=30) durchgeführt und berechnet mit welcher Wahrscheinlichkeit ein Angreifer erfolgreich die richtige Antwort auf die Frage erraten kann. Wir zeigen, dass ein Großteil der Transaktionen (> 94%) geschützt werden kann während das System selbst nutzbar bleibt.
Advanced Persistent Threats (APTs) are one of the main challenges in modern computer security. They are planned and performed by well-funded, highly-trained and often state-based actors. The first step of such an attack is the reconnaissance of the target. In this phase, the adversary tries to gather as much intelligence on the victim as possible to prepare further actions. An essential part of this initial data collection phase is the identification of possible gateways to intrude the target.
In this paper, we aim to analyze the data that threat actors can use to plan their attacks. To do so, we analyze in a first step 93 APT reports and find that most (80 %) of them begin by sending phishing emails to their victims. Based on this analysis, we measure the extent of data openly available of 30 entities to understand if and how much data they leak that can potentially be used by an adversary to craft sophisticated spear phishing emails. We then use this data to quantify how many employees are potential targets for such attacks. We show that 83 % of the analyzed entities leak several attributes of uses, which can all be used to craft sophisticated phishing emails.
In the modern Web, service providers often rely heavily on third parties to run their services. For example, they make use of ad networks to finance their services, externally hosted libraries to develop features quickly, and analytics providers to gain insights into visitor behavior.
For security and privacy, website owners need to be aware of the content they provide their users. However, in reality, they often do not know which third parties are embedded, for example, when these third parties request additional content as it is common in real-time ad auctions.
In this paper, we present a large-scale measurement study to analyze the magnitude of these new challenges. To better reflect the connectedness of third parties, we measured their relations in a model we call third party trees, which reflects an approximation of the loading dependencies of all third parties embedded into a given website. Using this concept, we show that including a single third party can lead to subsequent requests from up to eight additional services. Furthermore, our findings indicate that the third parties embedded on a page load are not always deterministic, as 50 % of the branches in the third party trees change between repeated visits. In addition, we found that 93 % of the analyzed websites embedded third parties that are located in regions that might not be in line with the current legal framework. Our study also replicates previous work that mostly focused on landing pages of websites. We show that this method is only able to measure a lower bound as subsites show a significant increase of privacy-invasive techniques. For example, our results show an increase of used cookies by about 36 % when crawling websites more deeply.
The European General Data Protection Regulation (GDPR), which went into effect in May 2018, brought new rules for the processing of personal data that affect many business models, including online advertising. The regulation’s definition of personal data applies to every company that collects data from European Internet users. This includes tracking services that, until then, argued that they were collecting anonymous information and data protection requirements would not apply to their businesses.
Previous studies have analyzed the impact of the GDPR on the prevalence of online tracking, with mixed results. In this paper, we go beyond the analysis of the number of third parties and focus on the underlying information sharing networks between online advertising companies in terms of client-side cookie syncing. Using graph analysis, our measurement shows that the number of ID syncing connections decreased by around 40 % around the time the GDPR went into effect, but a long-term analysis shows a slight rebound since then. While we can show a decrease in information sharing between third parties, which is likely related to the legislation, the data also shows that the amount of tracking, as well as the general structure of cooperation, was not affected. Consolidation in the ecosystem led to a more centralized infrastructure that might actually have negative effects on user privacy, as fewer companies perform tracking on more sites.
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.
In dieser Arbeit wird eine ganzheitliche Bedrohung für Business-Chat-Anwendungen aufgezeigt und bewertet: Chishing – Phishing über Business-Chats. Die Bedrohung hat ihren Ursprung in den Anfängen der heutigen vernetzten Welt und das zugrunde liegende Problem wird als Spoofing in seiner einfachsten Form bezeichnet. In vier von sechs Business-Chat-Tools, die in dieser Arbeit analysiert werden, ist es möglich, Anzeigenamen, Profilbilder und weitere persönliche Informationen erfolgreich zu fälschen. Dies stellt eine Bedrohung für Unternehmen dar, da es Insider-Bedrohungen Vorschub leistet und unter Umständen auch externen Entitäten dazu einlädt, sich als interne Mitarbeiterin auszugeben.
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.
Damit die medizinische Versorgung weiterhin flächendeckend gewährleistet werden kann und den explodierenden Kosten Einhalt geboten wird, muss ein Gesundheitswesen der Zukunft auf digitalen Technologien basieren. Die Kritikalität der entsprechenden Health-Services ruft Cyber-Sicherheit auf den Plan – die Sensibilität der im Gesundheitswesen verarbeiteten Daten den Datenschutz. Ein zukunftsfähiges Gesundheitswesen braucht einen stringenten Rechtsrahmen, eine moderne cloudbasierte Telematikinfrastruktur, die je nach Sicherheitsbedarf in verschiedenen Modellen umgesetzt werden kann, einen restriktiven Umgang mit globalen Public-Cloud-Providern, eine besonders gesicherte, leistungsstarke Forschungsdateninfrastruktur – etwa zur Optimierung von KI-Fähigkeiten, sichere Gesundheitsanwendungen und einiges mehr. Hier ein Ausblick.
Measurement studies are essential for research and industry alike to understand the Web’s inner workings better and help quantify specific phenomena. Performing such studies is demanding due to the dynamic nature and size of the Web. An experiment’s careful design and setup are complex, and many factors might affect the results. However, while several works have independently observed differences in
the outcome of an experiment (e.g., the number of observed trackers) based on the measurement setup, it is unclear what causes such deviations. This work investigates the reasons for these differences by visiting 1.7M webpages with five different measurement setups. Based on this, we build ‘dependency trees’ for each page and cross-compare the nodes in the trees. The results show that the measured trees differ considerably, that the cause of differences can be attributed to specific nodes, and that even identical measurement setups can produce different results.
Vor vier Jahren betrat die Datenschutz-Grundverordnung (DS-GVO) die Bühne und brachte für Unternehmen und Nutzer gleichermaßen Veränderungen mit sich. Doch gerade im dynamischen Umfeld des Online-Marketings tauchen ständig neue und oft knifflige Fragen auf – Fragen, die nun im Rahmen einer wissenschaftlichen Studie etwas genauer unter die Lupe genommen wurden.
Cookie notices (or cookie banners) are a popular mechanism for websites to provide (European) Internet users a tool to choose which cookies the site may set. Banner implementations range from merely providing information that a site uses cookies over offering the choice to accepting or denying all cookies to allowing fine-grained control of cookie usage. Users frequently get annoyed by the banner’s pervasiveness as they interrupt “natural” browsing on the Web. As a remedy, different browser extensions have been developed to automate the interaction with cookie banners.
In this work, we perform a large-scale measurement study comparing the effectiveness of extensions for “cookie banner interaction.” We configured the extensions to express different privacy choices (e.g., accepting all cookies, accepting functional cookies, or rejecting all cookies) to understand their capabilities to execute a user’s preferences. The results show statistically significant differences in which cookies are set, how many of them are set, and which types are set—even for extensions that aim to implement the same cookie choice. Extensions for “cookie banner interaction” can effectively reduce the number of set cookies compared to no interaction with the banners. However, all extensions increase the tracking requests significantly except when rejecting all cookies.