Mixing services try to distort cash flow tracking of cryptocurrencies and obfuscate the origin of customers’ earnings by substituting customers’ cryptocurrency funds with the funds of other customers or the mixers’ private assets. This quality makes mixing services interesting for money laundering, and they are therefore often used by criminals. As such, there is an urgent need to systematically understand how to restore the relationship between deposits and payouts of centralized mixing services. Unfortunately, there is minimal knowledge of how mixing processes of centralized mixing services work, and few attempts exist to create these demixing methods. This research aimed to develop a demixing method for centralized mixers with knowledge gained from ground-truth data. The ground-truth data contains information on orders and the transaction history of mixing service BestMixer. Demixing consists of collecting all addresses that are part of the mixer (attribution) and finding the correct payout to a deposit (reconstruction). Multiple statistical analysis techniques were applied to this data to verify existing attribution heuristics and find new characteristics of mixing services. Also, filtering techniques to reconstruct the relation between deposits and payouts were tested on the order data. This research verifies that BestMixer likely did not reuse addresses in the mixing process. It also showed that the lifespan of most addresses was shorter than 24 hours. In addition, many BestMixer addresses received or sent a transaction to another BestMixer address, which created sequences of BestMixer transactions. The sequences show that the mixer used a peeling chain pattern in combination with multi-input transactions. These characteristics can be used to attribute other centralized mixing services. The results also show that the mixer increased in popularity throughout time.
Overall, the reconstruction attempt with filtering techniques did not perform well on BestMixer orders, as it returned an impracticable amount of possible payout combinations. The mixer showed less activity in the beginning days of the service, and there are signs that the reconstruction works better in this earlier stage of the mixer. This means that when a mixer becomes more popular, it could become more difficult to demix the orders correctly.
From this research can be concluded that the ground-truth data of BestMixer does help in developing attribution heuristics for centralized mixers, but not in developing a general reconstruction method that correctly restores the relation between deposits and payouts, thus not suffice in demixing centralized mixers.

As software security expert Bruce Schneier argues, the pervasive vulnerability of embedded systems today is structurally similar to the security crisis of PCs in the mid-1990s—only much worse. Embedded devices are ideal malware targets for several reasons. Firstly, Internet-connected devices are inherently more exposed to remote exploitation. Furthermore, embedded systems are notoriously difficult to update, regularly leading to unpatched vulnerabilities. Last but not least, many such devices operate in a mostly unattended fashion, which means that the timely discovery of compromise is unlikely. Hardening is the process of securing a system by reducing its surface of vulnerability. Hardening of the already deployed embedded devices that are connected to the internet is examined in this research. A method capable of automatically generating and enforcing security configuration based on the embedded system’s set of functions has been designed and implemented. The proposed system is dynamic, automatic, and seamless. Hardening level of such devices is measured through recognized security benchmarks. The objective is to harden the product as much as possible while maintaining its full functionality. This study concerns embedded systems using custom Linux-distribution software. The thesis was conducted in cooperation with Atos. OpenScape Business series X (OSBiz X) was used as a case study. OSBiz X is an embedded system used as a telephony center, with more than 150.000 systems already deployed worldwide. Implementing the system described above on OSBiz X significantly increased the hardening-level of the product while its functionality remained intact. Due to the case study’s scenario results, Atos plans to integrate the proposed system into the next version of OSBiz X’s official release. Finally, ongoing research about other internal organization’s products that could greatly benefit from this approach is being conducted.

Background: A lot of scientists have tried to shed light on dark web markets. They did this by scraping these marketplaces over a period of time and describe what they were seeing. However, the methods used to measure these markets were never validated before. Research goal: This research will identify and validate the methods that are used in the literature to measure darknet marketplaces. Methods: To validate these methods, a novel dataset is used, namely the confiscated backend of a market. This dataset is cleaned and used to analyze the accuracy of these proxies on. Results: It is found that the number of transactions, revenue, and market share can be estimated with a high amount of explained variance. The predictions are precise enough to find prominent vendors on the market. The designed method of calculating the illegally obtained profits of a darknet vendor is easy to understand and could be used by law enforcement agencies. Finally, it is found that some vendors register to a market early as a strategy to ensure their business continuity.

Developing malware variants is extremely cheap for attackers because of the availability of various obfuscation tools. These variants can be grouped in malware families, based on information retrieved from their static and dynamic analysis. Dynamic, network-level analysis of malware shows its core behavior since it captures the interaction with its developer. On the other hand, increasingly more emphasis is given to using Deep Packet Inspection (DPI) in order to cluster malware’s network behavior. However, DPI has severe privacy implications, as it involves inspecting payloads of the network traffic.

This thesis presents an exploratory study, the aim of which is to characterize and cluster malware behavior using high-level, non-privacy-invasive, sequential features extracted from its network activity. The key intuition behind the proposed solution is that if the underlying infrastructure of distinct malware samples is similar, the order in which they perform certain actions should also be similar. The results of this research show that sequence clustering allows flexible and robust clusters, as opposed to using non-sequential features. The clusters themselves reveal interesting attacking capabilities, such as port scans, and the same Command and Control server responding to different malware families. Lastly, a comparison with clusters obtained from static analysis reveals that network-based clustering is far more qualified to determine the many behaviors exhibited by a single malware family, as well as behaviors common across multiple malware families.