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Abstract-We analyze the software stack of popular mobile advertising libraries on Android and investigate how they protect the users of advertising-supported apps from malicious advertising. We find that, by and large, Android advertising libraries properly separate the privileges of the ads from the host app by confining ads to dedicated browser instances that correctly apply the same origin policy.We then demonstrate how malicious ads can infer sensitive information about users by accessing external storage, which is essential for media-rich ads in order to cache video and images. Even though the same origin policy prevents confined ads from reading other apps' external-storage files, it does not prevent them from learning that a file with a particular name exists. We show how, depending on the app, the mere existence of a file can reveal sensitive information about the user. For example, if the user has a pharmacy price-comparison app installed on the device, the presence of external-storage files with certain names reveals which drugs the user has looked for.We conclude with our recommendations for redesigning mobile advertising software to better protect users from malicious advertising.

The Dark Web is notorious for being a major distribution channel of harmful content as well as unlawful goods. Perpetrators have also used cryptocurrencies to conduct illicit financial transactions while hiding their identities. The limited coverage and outdated data of the Dark Web in previous studies motivated us to conduct an in-depth investigative study to understand how perpetrators abuse cryptocurrencies in the Dark Web. We designed and implemented MFScope, a new framework which collects Dark Web data, extracts cryptocurrency information, and analyzes their usage characteristics on the Dark Web. Specifically, MFScope collected more than 27 million dark webpages and extracted around 10 million unique cryptocurrency addresses for Bitcoin, Ethereum, and Monero. It then classified their usages to identify trades of illicit goods and traced cryptocurrency money flows, to reveal black money operations on the Dark Web. In total, using MFScope we discovered that more than 80% of Bitcoin addresses on the Dark Web were used with malicious intent; their monetary volume was around 180 million USD, and they sent a large sum of their money to several popular cryptocurrency services (e.g., exchange services). Furthermore, we present two real-world unlawful services and demonstrate their Bitcoin transaction traces, which helps in understanding their marketing strategy as well as black money operations.

Abstract. DNS cache poisoning is a serious threat to today's Internet. We develop a formal model of the semantics of DNS caches, including the bailiwick rule and trust-level logic, and use it to systematically investigate different types of cache poisoning and to generate templates for attack payloads. We explain the impact of the attacks on DNS resolvers such as BIND, MaraDNS, and Unbound and their implications for several defenses against DNS cache poisoning.

Binary code similarity analysis (BCSA) is widely used for diverse security applications, including plagiarism detection, software license violation detection, and vulnerability discovery. Despite the surging research interest in BCSA, it is significantly challenging to perform new research in this field for several reasons. First, most existing approaches focus only on the end results, namely, increasing the success rate of BCSA, by adopting uninterpretable machine learning. Moreover, they utilize their own benchmark, sharing neither the source code nor the entire dataset. Finally, researchers often use different terminologies or even use the same technique without citing the previous literature properly, which makes it difficult to reproduce or extend previous work. To address these problems, we take a step back from the mainstream and contemplate fundamental research questions for BCSA. Why does a certain technique or a certain feature show better results than the others? Specifically, we conduct the first systematic study on the basic features used in BCSA by leveraging interpretable feature engineering on a large-scale benchmark. Our study reveals various useful insights on BCSA. For example, we show that a simple interpretable model with a few basic features can achieve a comparable result to that of recent deep learning-based approaches. Furthermore, we show that the way we compile binaries or the correctness of underlying binary analysis tools can significantly affect the performance of BCSA. Lastly, we make all our source code and benchmark public and suggest future directions in this field to help further research.