Wisdom of the crowds is the idea that groups of people can collectively make wise decisions. Research suggests that these crowds can even outsmart experts. To gather the wisdom of the crowds, this project utilizes a prediction market. To successfully gather the wisdom of the crowds, a predictionmarket has to overcome serious challenges, such as gathering a large and active user base, and deciding on a fair initialmarket value. The main goal of the project is to create a prediction market that can overcome these challenges and successfully gather the wisdom of the crowds. Research has been done in the field of prediction markets. This process started with researching the theory behind prediction markets, the wisdom of the crowds. After that evaluating existing prediction markets and reviewing literature related to those markets was useful. Before and during the research phase, clear goals were set for the project, together with a clear set of requirements. These goals can be divided into: leveraging the wisdomof the crowd, solving problems associated with predictionmarkets and developing a product that is easily maintainable. The final product reaches the goals of the project and meets the requirements. The prediction market correctly aggregates the estimations of users on the market, and provides probabilities on real-world events. These probabilities are contained in the values on the market. The prediction markets solves the problems encountered on other prediction markets. The project makes use of gamification, an automated marketmaker and a reward system to correctly initialise market values. The system was thoroughly tested and developed with maintainability in mind.

The Internet has grown from a few interconnections of trusted parties to an incredibly large network with many different use cases. While the Internet grew, threats emerged as well. Although there are many different threats on the Internet, Distributed Denial of Service (DDoS) attacks are a threat that keeps rising in the threat landscape. The asymmetry between adversaries and defenders is enormous - whereas DDoS attack can be started for less than 5$, DDoS prevention takes up the majority of operational cost in data centers and damages are in the billions. Thus, it is of vital importance that more effective methods of DDoS prevention are found and implemented to improve defensive effectiveness and to reduce costs. DDoS attacks consist of various types, but the largest share of DDoS attacks are of the subtype Distributed Reflected Denial of Service (DRDoS) attacks. Adversaries that execute DRDoS attacks use vulnerable servers to create incredibly large attacks and to stay anonymous. Previous work by Rossow showed us which vulnerable services are typically used for these DRDoS attacks, and how well these vulnerable services are exploitable for these attacks. However, we do not know why an adversary uses one vulnerable service but not another. Thus, this work fills that research gap by researching how adversaries react to differently configured vulnerable services, using a large scale experiment. This work shows that the amplification factor of a honeypot is a primary factor that determines whether an adversary will use a vulnerable server in an attack or not. This work also shows that attackers do not distinguish between regular vulnerable servers and their obvious honeypot counterparts. Furthermore, the response time of a system is of no influence, and some honeypots with packet loss attract fewer adversaries. Additionally, different attacks can be detected while using different service providers and geographical locations for honeypot deployment. Finally, the MD-honeypot framework that was developed for this research may be further developed into fully-fledged DRDoS mitigation software.

Proper security mechanism are a crucial part of safe usage of Implantable Medical Devices. Multiple researchers presented various solutions to address this problem, basing them on different underlying principles. Within the scope of this thesis we perform a security analysis of the chosen authentication protocols. What is more, we present a new attack on a scheme based on physiological signal processing using a fuzzy vault cryptographic primitive. We exploit the fact that the signal generated by the heart beats does not change sufficiently in the frequency domain. Therefore it is possible that the adversary reuses signal recorded at some earlier point of time to authenticate to the implant in real time. We show in an experimental way that it is able to break the scheme with probability reaching 75%. Finally, we propose a novel lightweight authentication protocol based on hash chains. To ensure the applicability of our work, we have decided to use only energy efficient solutions, that is hash functions and block ciphers. In contrast to existing work, we have extended the threat model and considered the implant reader distrusted. We present a set of energy measurements to provide advantages of different elements to be used during implementation of our solution.

Cryptojacking, a phenomenon also known as drive-by cryptomining, involves stealing computing power from others to be used in illicit cryptomining. While first observed as host-based infections with low activity, the release of an efficient browser-based cryptomining application -- as introduced by Coinhive in 2017 -- has skyrocketed cryptojacking activity in recent years. This novel method of monetizing Web activity attracted both website owners and cybercriminals seeking new methods to profit from. Website owners installed a cryptominer on their domains, while cybercriminals deployed cryptominers in large campaigns spread over numerous domains. Several studies developed detection methods to identify these browser-based cryptominers on websites, but none of these studies focused on the extent and coordination of campaigns deployed by adversaries. Furthermore, the prevalence of cryptojacking on websites is not well estimated yet and the potentially largest attack vector -- a man-in-the-middle attack -- has never been researched before.
In this thesis, we perform multiple large studies on cryptojacking to fill these gaps. After crawling a random sample of 49M domains, 20% of the Internet, we conclude that cryptojacking is present on 0.011% of all domains and that adult content is the most prevalent category of websites affected. We show that this percentage is significantly larger in the popular part of the Internet. This led to the conclusion that surveying solely domains listed in the Alexa Top 1M to estimate cryptojacking prevalence results in an overestimation of the problem. Furthermore, we show that infection rates on different Top Level Domains (TLDs) differ widely, as the Russian zone is home to a disproportionate number of cryptojacking domains, while other large TLDs -- such as .com -- show a significantly lower number of infections.
In another crawl, we have identified 204 cryptojacking campaigns on websites, an order of magnitude more than previous work, which indicates that the extent of these campaigns is heavily underestimated. The results of the two crawls combined reveal that 48% of all cryptojacking activity on websites is organized. The identified campaigns ranged in sizes from only 5 to 987 websites and we discovered that cybercriminals have chosen third-party software -- such as WordPress and Drupal -- as their method of choice for spreading cryptojacking infections efficiently. With a novel method of using NetFlow data recorded in a Tier 1 network, we estimated the popularity of mining applications, which showed that while Coinhive has a larger installed base, CoinImp WebSocket proxies were digesting significantly more traffic in the second half of 2018.
We have reported about a new attack vector that drastically overshadows all other cryptojacking activity. Through a firmware vulnerability in MikroTik routers, cybercriminals are able to rewrite outgoing user traffic and embed cryptomining code in every outgoing Web connection. Thus, every Web page visited by any user behind an infected router would mine to profit the adversaries. Based on the aforementioned NetFlow data, weekly third-party crawls and network telescope traffic, we were able to follow their activities over a period of 10 months. We report on the modus operandi and coordinating infrastructure of the perpetrators, which were during this period in control of up to 1.4M routers, which is approximately 70% of all MikroTik devices deployed in the world. During the peak of this attack, more than 440K routers were infected concurrently.
We have discovered that half of the infected routers are patched within 18 days after compromise, but 30% of the infections last longer than 50 days. Additionally, we observed different levels of sophistication among adversaries, ranging from individual installations to campaigns involving large numbers of routers. The combination of datasets allowed us to link tens of seemingly different infections to one actor.
Our analysis of cryptojacking with a focus on organized campaigns has shown that cybercriminals have successfully discovered a new method for monetary gain. With the discontinuation of Coinhive due to decreased Monero prices in March 2019, the cryptojacking landscape has changed enormously, and we are curious who will fill this power vacuum. As browser-based mining is not anywhere near as profitable as it was in early 2018, we believe that singular cryptojacking activity -- by individual website owners -- will decrease. However, we expect adversaries to find possibilities of deploying cryptojacking at an even larger scale to still be profitable. This stresses the importance of researching campaigns, as the reuse of techniques, tactics and procedures in deploying them provides an effective angle to detect and mitigate these malicious activities. With prices decreasing throughout 2018, one would expect that this problem will eventually solve itself. Apart from the discontinuation of Coinhive, there is no clear indication that this is the case, as Monero prices have started to recover in the first months of 2019. If this trend continues, we expect to experience another outbreak of large cryptojacking campaigns, as robust defenses are still not widely implemented.

Many processes rely on the availability of the Internet. The Border Gateway Protocol (BGP) is widely used for exchanging routing information between routers and is essential for the successful operation of the Internet. Because BGP has not been designed with security in mind, BGP anomalies such as origin hijacks, route leaks, and link failure often occur. This research proposes a detection system for detecting origin hijacks, which is one of themost common anomalies seen on the Internet. Our detection system uses a filter-based approach. Each filter attempts to validate announcements seen by our detection system. Announcements that could not be verified by any filter are seen as origin hijacks. Because of this approach, origin hijacks that would otherwise be missed by other solutions will be detected. We use multiple data sources such as RIR Statistics Exchange Format Listings (RSEF), routing registries, RPKI Route Origin Authorizations (ROAs) and CAIDA’s AS relationship dataset in order to validate announcements. Upon running our detection system on 29 days of BGP traffic, we were able to detect 902 origin hijacks. 83% of the detected origin hijacks had a lifespan of fewer than 2.5 hours which strongly suggests that these were undesired announcements.

In today's world, the Internet is the backbone of our society. The relatively unknown Border Gateway Protocol (BGP), and with its vulnerabilities, gives malicious parties an opportunity for abuse. By improving the currently known AS relation data set and by simulating BGP traffic this abuse is better spotted. Kastelein's topology generating algorithm outperforms the state-of-the-art topology generating algorithms and state-of-the-art AS relation data sets. The proposed BGP simulator misses vital information such as LOCAL_PREFERENCE values to accurately simulate BGP traffic. This lack of information results in longer BGP paths that are not matched with BGP paths from route collectors.

Android smartphones collect and compile a huge amount of sensitive information which is secured using cryptography. There is an unintended leakage of information during the physical implementation of a cryptosystem on a device. Such a leakage is often termed as side channel and is used to break the implementation of cryptographic algorithms. In this work, we utilize cache memory based side channels on android smartphones to retrieve crypto-process information. These side channels are based on the information leakage through the operating system, micro-architecture of the processor and the state of the processor's memory cache. We demonstrate the retrieval of data dependent memory access patterns using a spy application running in the background to recover the full secret key of cryptographic primitives such as AES T-table implementation in OpenSSL, all that would be necessary is a rogue app downloaded from an app store that is run under normal privileges.
We show that a mathematical correlation which depends on the guessed key and can be utilized to recover the \emph{complete} key in access-driven cache attacks (CAs). We show the effectiveness of the proposed method using access time measured in noisy environments. We analyze the changes in the correlation values with the number of plaintexts/ciphertexts for a successful attack using key estimation. Furthermore, we discuss and demonstrate the applicability of cache memory based side channel attacks on a white-box implementation of AES.

Nowadays, organization networks are facing an increased number of different attacks and existing intrusion and anomaly detection systems fail to keep up. By focusing on security policies, malicious signatures or generic network characteristics, existing systems are not able to cover the full landscape of attacks. In this thesis we try to tackle the problem of anomaly detection on a user network behavior level and an application level. In the proposed framework, network traffic is first separated into different flows based on the mobile application it originates from. Moving forward, the processed network flows are used as input for a flexible noise tolerant behavior modeling framework. The proposed framework is based on density based clustering and tries to identify temporal changes in the user behavior that qualify as anomalous. Moreover, we utilize the model to identify behavioral patterns shared by users and analyze the temporal consistency of user network behavior. To evaluate the framework performance, real network mobile traffic provided a private organization is used. The framework validation is performed by combining the captured network traffic with a conducted employee survey. Overall, the system is able to accurately follow changes in the user behavior based on each application, identify anomalies as well as provide insight on shared behaviors or reoccurring behavioral patterns.

Dark silicon is pushing processor vendors to add more specialized units such as accelerators to commodity processor chips. Unfortunately this is done without enough care to security. In this paper we look at the security implications of integrated Graphical Processor Units (GPUs) found in almost all mobile processors. We demonstrate that GPUs, already widely employed to accelerate a variety of benign applications such as image rendering, can also be used to “accelerate” microarchitectural attacks (i.e., making them more effective) on commodity platforms. In particular, we show that an attacker can build all the necessary primitives for performing effective GPU-based microarchitectural attacks and that these primitives are all exposed to the web through standardized browser extensions, allowing side-channel and Rowhammer attacks from JavaScript. These attacks bypass state-of-the-art mitigations and advance existing CPU-based attacks: we show the first end-to-end microarchitectural compromise of a browser running on a mobile phone by orchestrating our GPU primitives. While powerful, these GPU primitives are not easy to implement due to undocumented hardware features. We describe novel reverse engineering techniques for peeking into the previously unknown cache architecture and replacement policy of the Adreno 330, an integrated GPU found in many common mobile platforms. This information is necessary when building shader programs implementing our GPU primitives. We conclude by discussing mitigations against GPU-enabled attackers.