arXiv subjects

Thorsten Holz

Publications and source records attributed to Thorsten Holz.

63 records · Page 4Linked to original sources

arXiv2019

We Value Your Privacy ... Now Take Some Cookies: Measuring the GDPR's Impact on Web Privacy

The European Union's General Data Protection Regulation (GDPR) went into effect on May 25, 2018. Its privacy regulations apply to any service and company collecting or processing personal data in Europe. Many companies had to adjust their data handling processes, consent forms, and privacy policies to comply with the GDPR's transparency requirements. We monitored this rare event by analyzing the GDPR's impact on popular websites in all 28 member states of the European Union. For each country, we periodically examined its 500 most popular websites - 6,579 in total - for the presence of and updates to their privacy policy. While many websites already had privacy policies, we find that in some countries up to 15.7 % of websites added new privacy policies by May 25, 2018, resulting in 84.5 % of websites having privacy policies. 72.6 % of websites with existing privacy policies updated them close to the date. Most visibly, 62.1 % of websites in Europe now display cookie consent notices, 16 % more than in January 2018. These notices inform users about a site's cookie use and user tracking practices. We categorized all observed cookie consent notices and evaluated 16 common implementations with respect to their technical realization of cookie consent. Our analysis shows that core web security mechanisms such as the same-origin policy pose problems for the implementation of consent according to GDPR rules, and opting out of third-party cookies requires the third party to cooperate. Overall, we conclude that the GDPR is making the web more transparent, but there is still a lack of both functional and usable mechanisms for users to consent to or deny processing of their personal data on the Internet.

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arXiv2019

A Study of Newly Observed Hostnames and DNS Tunneling in the Wild

The domain name system (DNS) is a crucial backbone of the Internet and millions of new domains are created on a daily basis. While the vast majority of these domains are legitimate, adversaries also register new hostnames to carry out nefarious purposes, such as scams, phishing, or other types of attacks. In this paper, we present insights on the global utilization of DNS through a measurement study examining exclusively newly observed hostnames via passive DNS data analysis. We analyzed more than two billion such hostnames collected over a period of two months. Surprisingly, we find that only three second-level domains are responsible for more than half of all newly observed hostnames every day. More specifically, we found that Google's Accelerated Mobile Pages (AMP) project, the music streaming service Spotify, and a DNS tunnel provider generate the majority of new domains on the Internet. DNS tunneling is a covert channel technique to transfer arbitrary information over DNS via DNS queries and answers. This technique is often (ab)used by attackers to transfer data in a stealthy way, bypassing traditional network security systems. We find that potential DNS tunnels cause a significant fraction of the global DNS requests for new hostnames: our analysis reveals that nearly all resource record type NULL requests and more than a third of all TXT requests can be attributed to DNS tunnels. Motivated by these empirical measurement results, we propose and implement a method to identify DNS tunnels via a step-wise filtering approach that relies on general characteristics of such tunnels (e.g., number of subdomains or resource record type). Using our approach on empirical data, we successfully identified 273 suspicious domains related to DNS tunnels, including two known APT campaigns (Wekby and APT32).

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arXiv2018

The Unwanted Sharing Economy: An Analysis of Cookie Syncing and User Transparency under GDPR

The European General Data Protection Regulation (GDPR), which went into effect in May 2018, leads to important changes in this area: companies are now required to ask for users' consent before collecting and sharing personal data and by law users now have the right to gain access to the personal information collected about them. In this paper, we study and evaluate the effect of the GDPR on the online advertising ecosystem. In a first step, we measure the impact of the legislation on the connections (regarding cookie syncing) between third-parties and show that the general structure how the entities are arranged is not affected by the GDPR. However, we find that the new regulation has a statistically significant impact on the number of connections, which shrinks by around 40%. Furthermore, we analyze the right to data portability by evaluating the subject access right process of popular companies in this ecosystem and observe differences between the processes implemented by the companies and how they interpret the new legislation. We exercised our right of access under GDPR with 36 companies that had tracked us online. Although 32 companies (89%) we inquired replied within the period defined by law, only 21 (58%) finished the process by the deadline set in the GDPR. Our work has implications regarding the implementation of privacy law as well as what online tracking companies should do to be more compliant with the new regulation.

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arXiv2018

Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding

Voice interfaces are becoming accepted widely as input methods for a diverse set of devices. This development is driven by rapid improvements in automatic speech recognition (ASR), which now performs on par with human listening in many tasks. These improvements base on an ongoing evolution of DNNs as the computational core of ASR. However, recent research results show that DNNs are vulnerable to adversarial perturbations, which allow attackers to force the transcription into a malicious output. In this paper, we introduce a new type of adversarial examples based on psychoacoustic hiding. Our attack exploits the characteristics of DNN-based ASR systems, where we extend the original analysis procedure by an additional backpropagation step. We use this backpropagation to learn the degrees of freedom for the adversarial perturbation of the input signal, i.e., we apply a psychoacoustic model and manipulate the acoustic signal below the thresholds of human perception. To further minimize the perceptibility of the perturbations, we use forced alignment to find the best fitting temporal alignment between the original audio sample and the malicious target transcription. These extensions allow us to embed an arbitrary audio input with a malicious voice command that is then transcribed by the ASR system, with the audio signal remaining barely distinguishable from the original signal. In an experimental evaluation, we attack the state-of-the-art speech recognition system Kaldi and determine the best performing parameter and analysis setup for different types of input. Our results show that we are successful in up to 98% of cases with a computational effort of fewer than two minutes for a ten-second audio file. Based on user studies, we found that none of our target transcriptions were audible to human listeners, who still understand the original speech content with unchanged accuracy.

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arXiv2018

On Security Research Towards Future Mobile Network Generations

Over the last decades, numerous security and privacy issues in all three active mobile network generations have been revealed that threaten users as well as network providers. In view of the newest generation (5G) currently under development, we now have the unique opportunity to identify research directions for the next generation based on existing security and privacy issues as well as already proposed defenses. This paper aims to unify security knowledge on mobile phone networks into a comprehensive overview and to derive pressing open research questions. To achieve this systematically, we develop a methodology that categorizes known attacks by their aim, proposed defenses, underlying causes, and root causes. Further, we assess the impact and the efficacy of each attack and defense. We then apply this methodology to existing literature on attacks and defenses in all three network generations. By doing so, we identify ten causes and four root causes of attacks. Mapping the attacks to proposed defenses and suggestions for the 5G specification enables us to uncover open research questions and challenges for the development of next-generation mobile networks. The problems of unsecured pre-authentication traffic and jamming attacks exist across all three mobile generations. They should be addressed in the future, in particular, to wipe out the class of downgrade attacks and, thereby, strengthen the users' privacy. Further advances are needed in the areas of inter-operator protocols as well as secure baseband implementations. Additionally, mitigations against denial-of-service attacks by smart protocol design represent an open research question.

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arXiv2018

RAPTOR: Ransomware Attack PredicTOR

Ransomware, a type of malicious software that encrypts a victim's files and only releases the cryptographic key once a ransom is paid, has emerged as a potentially devastating class of cybercrimes in the past few years. In this paper, we present RAPTOR, a promising line of defense against ransomware attacks. RAPTOR fingerprints attackers' operations to forecast ransomware activity. More specifically, our method learns features of malicious domains by looking at examples of domains involved in known ransomware attacks, and then monitors newly registered domains to identify potentially malicious ones. In addition, RAPTOR uses time series forecasting techniques to learn models of historical ransomware activity and then leverages malicious domain registrations as an external signal to forecast future ransomware activity. We illustrate RAPTOR's effectiveness by forecasting all activity stages of Cerber, a popular ransomware family. By monitoring zone files of the top-level domain .top starting from August 30, 2016 through May 31, 2017, RAPTOR predicted 2,126 newly registered domains to be potential Cerber domains. Of these, 378 later actually appeared in blacklists. Our empirical evaluation results show that using predicted domain registrations helped improve forecasts of future Cerber activity. Most importantly, our approach demonstrates the value of fusing different signals in forecasting applications in the cyber domain.

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arXiv2017

An Empirical Study on Price Differentiation Based on System Fingerprints

Price differentiation describes a marketing strategy to determine the price of goods on the basis of a potential customer's attributes like location, financial status, possessions, or behavior. Several cases of online price differentiation have been revealed in recent years. For example, different pricing based on a user's location was discovered for online office supply chain stores and there were indications that offers for hotel rooms are priced higher for Apple users compared to Windows users at certain online booking websites. One potential source for relevant distinctive features are \emph{system fingerprints}, i.\,e., a technique to recognize users' systems by identifying unique attributes such as the source IP address or system configuration. In this paper, we shed light on the ecosystem of pricing at online platforms and aim to detect if and how such platform providers make use of price differentiation based on digital system fingerprints. We designed and implemented an automated price scanner capable of disguising itself as an arbitrary system, leveraging real-world system fingerprints, and searched for price differences related to different features (e.\,g., user location, language setting, or operating system). This system allows us to explore price differentiation cases and expose those characteristic features of a system that may influence a product's price.

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arXiv2004

Ermittlung von Verwundbarkeiten mit elektronischen Koedern

Electronic bait (honeypots) are network resources whose value consists of being attacked and compromised. These are often computers which do not have a task in the network, but are otherwise indestinguishable from regular computers. Such bait systems could be interconnected (honeynets). These honeynets are equipped with special software, facilitating forensic anylisis of incidents. Taking average of the wide variety of recorded data it is possible to learn considerable more about the behaviour of attackers in networks than with traditional methods. This article is an introduction into electronic bait and a description of the setup and first experiences of such a network deployed at RWTH Aachen University. ----- Als elektronische Koeder (honeypots) bezeichnet man Netzwerkressourcen, deren Wert darin besteht, angegriffen und kompromittiert zu werden. Oft sind dies Computer, die keine spezielle Aufgabe im Netzwerk haben, aber ansonsten nicht von regulaeren Rechnern zu unterscheiden sind. Koeder koennen zu Koeder-Netzwerken (honeynets) zusammengeschlossen werden. Sie sind mit spezieller Software ausgestattet, die die Forensik einer eingetretenen Schutzzielverletzung erleichtert. Durch die Vielfalt an mitgeschnittenen Daten kann man deutlich mehr ueber das Verhalten von Angreifern in Netzwerken lernen als mit herkoemmlichen forensischen Methoden. Dieser Beitrag stellt die Philosophie der Koeder-Netzwerke vor und beschreibt die ersten Erfahrungen, die mit einem solchen Netzwerk an der RWTH Aachen gemacht wurden.

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arXiv2004

NoSEBrEaK - Attacking Honeynets

It is usually assumed that Honeynets are hard to detect and that attempts to detect or disable them can be unconditionally monitored. We scrutinize this assumption and demonstrate a method how a host in a honeynet can be completely controlled by an attacker without any substantial logging taking place.

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