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Institute
Function detection is a well-known problem in binary analysis. While prior work has focused on Linux/ELF, Windows/PE binaries have only partially been considered. This paper introduces FuncPEval, a dataset for Windows x86 and x64 PE files, featuring Chromium and the Conti ransomware, along with ground truth data for 1,092,820 function starts. Utilizing FuncPEval, we evaluate five heuristics-based (Ghidra, IDA, Nucleus, rev.ng, SMDA) and three machine-learning-based (DeepDi, RNN, XDA) function start detection tools. Among these, IDA achieves the highest F1-score (98.44%) for Chromium x64, while DeepDi closely follows (97%) but stands out as the fastest. Towards explainability, we examine the impact of padding between functions on the detection results, finding all tested tools, except rev.ng, are susceptible to randomized padding. The randomized padding significantly diminishes the effectiveness of the RNN, XDA, and Nucleus. Among the learning-based tools, DeepDi exhibits the least sensitivity, while Nucleus is the most adversely affected among the non-learning-based tools.
Filter lists are used by various users, tools, and researchers to identify tracking technologies on the Web. These lists are created and maintained by dedicated communities. Aside from popular blocking lists (e.g., EasyList), the communities create region-specific blocklists that account for trackers and ads that are only common in these regions. The lists aim to keep the size of a general blocklist minimal while protecting users against region-specific trackers. In this paper, we perform a large-scale Web measurement study to understand how different region-specific filter lists (e.g., a blocklist specifically designed for French users) protect users when visiting websites. We define three privacy scenarios to understand when and how users benefit from these regional lists and what effect they have in practice. The results show that although the lists differ significantly, the number of rules they contain is unrelated to the number of blocked requests. We find that the lists’ overall efficacy varies notably. Filter lists also do not meet the expectation that they increase user protection in the regions for which they were designed. Finally, we show that the majority of the rules on the lists were not used in our experiment and that only a fraction of the rules would provide comparable protection for users.
Abstract
In this paper, we shed light on shared hosting services’ security and trust implications and measure their attack surfaces. To do so, we analyzed 30 shared hosters and found that all of them might leak relevant information, which could be abused unnoticed. An adversary could use this attack surface to covertly extract data from various third parties registered with a shared hoster. Furthermore, we found that most hosters suffer from vulnerabilities that can be used by an internal attacker (i.e., someone using the service) to compromise other hosted services or the entire system.
Abstract
For years, researchers have been analyzing mobile Android apps to investigate diverse properties such as software engineering practices, business models, security, privacy, or usability, as well as differences between marketplaces. While similar studies on iOS have been limited, recent work has started to analyze and compare Android apps with those for iOS. To obtain the most representative analysis results across platforms, the ideal approach is to compare their characteristics and behavior for the same set of apps, e. g., to study a set of apps for iOS and their respective counterparts for Android. Previous work has only attempted to identify and evaluate such cross-platform apps to a limited degree, mostly comparing sets of apps independently drawn from app stores, manually matching small sets of apps, or relying on brittle matches based on app and developer names. This results in (1) comparing apps whose behavior and properties significantly differ, (2) limited scalability, and (3) the risk of matching only a small fraction of apps.
In this work, we propose a novel approach to create an extensive dataset of cross-platform apps for the iOS and Android ecosystems. We describe an analysis pipeline for discovering, retrieving, and matching apps from the Apple App Store and Google Play Store that we used to create a set of 3,322 cross-platform apps out of 10,000 popular apps for iOS and Android, respectively. We evaluate existing and new approaches for cross-platform app matching against a set of reference pairs that we obtained from Google's data migration service. We identify a combination of seven features from app store metadata and the apps themselves to match iOS and Android apps with high confidence (95.82 %). Compared to previous attempts that identified 14 % of apps as cross-platform, we are able to match 34 % of apps in our dataset. To foster future research in the cross-platform analysis of mobile apps, we make our pipeline available to the community.
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.
This paper discusses the transformative potential of 6G technology and the tactile Internet in reshaping participatory healthcare models while architecturing these digital healthcare systems with security and resiliency by design. As healthcare continues to advance towards more inclusive and patient-centered approaches, the role of emerging technologies like mobile health, 6G, and the Internet will become increasingly significant in facilitating these interactions while ensuring the security and privacy of patient data. Furthermore, the organizations providing healthcare to patients must ensure compliance with different regulations, which are also focusing more and more on cybersecurity issues.
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.
Hybrid broadcast broadband television (HbbTV) is an evolving technology that connects linear TV with modern HTML5 applications, delivering extras like games, videos, and online shopping. However, its bidirectional transmission functionality raises privacy concerns, as it introduces new tracking methods for TV channels. While previous studies focused on security issues or user awareness of HbbTV privacy challenges, a detailed examination of the tracking and transparency mechanisms of the HbbTV ecosystem is still missing. This study fills this gap by extensively analyzing these features within the European HbbTV ecosystem, and in particular within German-language TV channels. We monitored more than 350 TV channels for over 400 hours, evaluating 1) prevalent HbbTV tracking methods, 2) consent notice prevalence and user interactions, and 3) privacy policy disclosures. Our findings indicate that the HbbTV tracking system operates independently of the Web, consent notices exploit system constraints to influence users, and privacy policies often do not align with actual data practices.
Phishing is an increasing threat to the security of end-users, networks, and organizations. Phishing simulations via email are a widespread tool used to measure user awareness, especially in workplace settings. However, current studies focusing on large-scale analysis of phishing simulations often have issues: The phishing simulations were conducted using a small sample size (mostly one or two organizations), or while many emails are sent, the analysis focuses only on specific companies. This study analyzes phishing simulations conducted over three years at 36 organizations with over 68 000 delivered emails. We compare different dimensions of the organizations where these simulations were conducted, such as the economic sector and departments. Furthermore, we evaluate various dimensions of phishing simulation campaigns, such as detection difficulty and the scenario under which the simulation occurs. Our findings indicate significant disparities in the results, such as the industry sector in which the company operates. Moreover, we find substantial differences between the success rates of varying scenarios used for phishing emails.

