Contributions published at Blockchain and Distributed Ledger Technologies (Claudio Tessone)

The rise of Blockchain technology and cryptocurrencies has enabled the creation of decentralized alternative payment systems, without the need for third party financial institutions. However, this decentralization and pseudo-anonymity have also facilitated the emergence of darknet markets (DNMs) offering illicit products. This study introduces the"grayscale diffusion framework", with the aim of modeling and understanding the propagation of dark assets in the Bitcoin network. Formulated and implemented with a combination of on-chain and off-chain data, this approach utilizes address clustering, haircut tainting, and community partitioning to offer a unique analysis perspective on dark asset proliferation across the Bitcoin blockchain. The framework uncovers interesting patterns in the assortative nature of Bitcoin transactions based on the darkness level of assets, pointing to the existence of non-random clusters and communities that facilitate dark asset diffusion. Our research not only addresses key questions related to the effective modeling and tracking of tainted asset flow, but also provides valuable insights. Keywords: Bitcoin, Address Clustering, Darknet Marketplaces, Gray-scale Diffusion, User Transaction Networks.

This thesis addresses the implementation of concepts outlined in FIFA's Enhanced Football Intelligence (EFI) document through an open-source library, filling the gap with accessible implementations for these concepts. The EFI document provides descriptions for various metrics related to football performance analysis used in the FIFA World Cup 2022. Existing packages in football analytics do not fully incorporate the latest methodologies used in the FIFA World Cup 2022, essential for the creation of a source that aligns with FIFA's definitions. The implemented concepts cover possession control, phases of play, ball recovery time, line breaks, receptions behind midfield and defensive lines, defensive line height and team length, team shape, final third entries, pressure on the ball, forced turnovers, and expected goals (xG). Utilizing the explanations of these concepts, the thesis formulates a main approach and involves refinements. The level of stability varies, with methods that incorporate fewer heuristics tending to be more stable, while those that rely on a greater number of heuristics tend to be less stable. However, during implementation, limitations were encountered, including the lack of technical details and absence of FIFA's resources regarding the technology they have employed. Specifically, the lack of heuristics mentioned in the definitions of the concepts was a notable gap. Challenges were also observed, such as specific matches that are labeled as outliers due to their performance in distinct concepts. Despite these limitations and challenges, the implementation overall offers stable and accurate performance, aligned with FIFA's outcomes. In future work, these limitations can be addressed through a comprehensive approach. Firstly, revisiting the concepts with additional information regarding their descriptions will enhance the understanding of the underlying factors. Secondly, the expansion of datasets will not only provide a broader foundation for analysis but also improve the heuristics employed, leading to enhanced accuracy and stability of the outcomes. Additionally, the application of advanced technologies, similar to those employed by FIFA, can significantly contribute to improving the reliability and effectiveness of the results. By considering these avenues, future research can overcome the identified limitations. This thesis contributes to advancing football performance analysis by addressing these challenges and provides a valuable resource for researchers, analysts, and football enthusiasts seeking to reproduce FIFA's match reports and gain insights into football performance. Keywords: Enhanced Football Intelligence, FIFA, open-source implementation, football performance analysis, sports analytics.

Decentralized finance (DeFi) has grown rapidly since 2020, but it also has seen a large correction in 2022. By the end of 2022, the total value locked in DeFi smart contracts has increased by a factor of almost 70, compared to 2020 (Nansen DeFi Statistics 2023). Due to open access, transparency, high interoperability and low intermediation, DeFi application are facing different circumstances than their traditional counterparts. The ecosystem has created new inventions and is still evolving. DeFi protocols are improving their services or adding new services to their portfolio in order to become platforms that offer an increased user experience. This thesis creates a taxonomy of decentralized finance protocols with goal to facilitate future research in this area. Additionally, a comparison to traditional financial applications is made in order to derive possible implications to traditional finance. Different approaches to loan issuance can be found. Even if there is no credit issuance or a securities market in DeFi, blockchain technology seems to offer some benefits in this field. Decentralized exchanges are usually designed differently to traditional order book exchanges. They are finding innovative ways to adopt traditional order book functionalities and under certain circumstances they can be beneficial over order book exchanges. Other DeFi inventions cannot be found in traditional finance. Inventions like flash loans, perpetual swaps and yield farming bring new possibilities to the DeFi ecosystem, but they also certain risks and have lead to several exploits. Risks and opportunities around these inventions are discussed in this thesis.

Polkadot’s validator selection process employs an iterative algorithm, which is dependent on the size of the staking system. As Polkadot’s staking network is growing, I propose a machine learning alternative approach to the current implementation, that is more independent of scale. The algorithm, the sequential Phragmén, aims to reduce a graph of nominator-validator edges to a subset of validators, the active set, and distribute the stake backing them, as evenly as possible. The goal of this thesis is to produce superior results, consequently improving the overall security or to provide solutions of equal quality in faster time. In order to achieve the goal, a pipeline is setup, that gathers data and transforms it such that it is suitable for machine learning models. Predictions are made, which are adjusted to fit the requirements set by Polkadot. The adjusted results are scored and ultimately compared to the solutions discovered by sequential Phragmén. An analysis of the training data reveals, that the active set remains highly static, with only 10 validators on average changing from era to era. This lack of diversity raised concerns regarding potential attack vectors for adversaries. Furthermore, it was observed that many nominators are acting inefficiently. Many of them do not execute their right to nominate up to 16 validators, which would maximize their chance of having a validator included in the active set. Additionally, many of them include validators, which are not eligible targets. This occurs since nominators frequently ignore their duty to actively tend to their validator preferences. They set them once and do not update them. Eligible validators become inactive (intentionally or unintentionally) and consequently remain as part of the nominators preferences. The prediction task was split up into three models: The first model predicts the next active set, the second model predicts the sum of stake each validator receives and the third predicts the individual stake distribution. The results show, that the first two models are trained well and produce satisfactory results. However, the learning curves of the third model reveal a bias, which make the predictions suboptimal. The source of the bias is likely the substantial changes in target values introduced by a slight shift of active set. We conclude that it is unlikely to outperform the sequential Phragmén using a supervised approach under the described conditions. Therefore, we recommend exploring an unsupervised approach for further research. Furthermore, we recommend the development of a tool for nominators, that could increase the convenience and the security of the overall staking system as a consequence.

This thesis aims to increase the capabilities of the Polkadot Data Preprocessing Pipeline of the Blockchain Observatory (BCO), a project of the Blockchain and Distributed Ledger Technologies Group (BDLT) at University of Zurich (UZH). This pipeline currently collects data of the Polkadot Relay chain. With the recent launch of Parachains, the Polkadot ecosystem expanded considerably. The aim of this thesis is to expand the pipeline to two Parachains, namely Moonbeam, a Ethereum Virtual Machine (EVM) compatible Parachain, and Interlay, a bridge to Bitcoin (BTC). Furthermore, Cross-Consensus Message Transfer (XCM) between the chains should also be handled. The new Pipelines consist of an archive node, a producer module and a preprocessing module per chain. The node provides raw data, the producer stores the raw data, while making sure the data is valid and checking and correcting the database integrity. The preprocessor finally preprocesses the raw data received, making use of the node for storage queries and web3 interactions in the case of Moonbeam. The data collected focuses on historical balance and transfer data, staking and reward data and data concerning the specific Parachains, such as ERC-20 Tokens on Moonbeam and vaults on Interlay. A part of the thesis was dedicated to the optimization of the pipeline to increase the speed of data collection by restructuring the preprocessor to only use batched queries per block processed. The memory footprint was reduced by removing redundant data. Finally, some queries and visualization are showcased to highlight interesting aspects of the data and to demonstrate the capabilities of the preprocessor, as well as providing examples.

The Internet of Things (IoT) has grown exponentially in recent years and it is predicted that the number of devices will double again to 30 billion by 2030 [24]. At the same time, the number of unpatched, vulnerable and infected devices connected to the Internet is increasing exponentially as well. Famous malware incidents from the past like Mirai have painfully illustrated how vulnerable IoT devices are on a broad scale. This work examines how Moving Target Defense (MTD) can be used in a collaborative framework for defense in depth and to thwart cyberattacks. For this purpose, a system prototype has been implemented that is capable of autonomously learning to defend a set of IoT devices (more specifically Radio Frequency Spectrum Sensors belonging to ElectroSense) from a specific set of malware by selecting and deploying Moving Target Defenses (MTDs). In scientific literature, usually individual MTDs optimized against specific attacks are presented, but no collaborative framework that combines and orchestrates a set of MTDs. In the prototypical implementation, an individual local agent is deployed on a set of simulated device, monitoring the behavior of its host, according to 100 system parameters. In case an attack is detected, the local agent is invoked in order to select from a set of MTD to ward off the attack. If the post-MTD device behavior can be considered normal again, the local agent receives a reward, which is used to update the local policy. Thanks to the use of FL, all local agents contribute to learning one global defense policy together. The project shows that a good attack mitigation probability can be achieved in non-federated as well as federated learning setting. Furthermore, the system also proves to be somewhat robust against locally and globally skewed sample distribution. Under certain assumptions it can also be assumed that collaborative learning of an MTD selection policy is faster and more robust than centralized learning. The findings on how FRL can be used in IT security to collaboratively learn an MTD selection policy contribute to the state of the art on MTD.

An integral virtue of distributed ledger technologies is their acclaimed higher level of decentralisation compared to traditional financial systems. Empirical literature, however, suggests that many systems tend towards centralisation as well, creating a gap between the common narrative and the actual state of these networks. This study expands the current literature by offering a first-time, basic analysis of the degree of decentralisation of the platform Hedera Hashgraph, a public permissioned distributed ledger technology, employing data directly fetched from a network node. The results show a considerably higher amount of released supply compared to the release schedule and a growing number of daily active accounts. Also, Hedera Hashgraph exhibits a high centralisation of wealth and a small core that acts as an intermediary in transactions for the rest of the network. However, the Nakamoto index and Theil index point to recent progress towards a more decentralised network in terms of network usage and wealth distribution.

Quantifying the security of the consensus mechanism is usually complicated and performed in either a theoretical or computational fashion. The advantage of the Proof of Work (PoW) consensus mechanism is that its Sybil resistance mechanism makes it computationally difficult for an attacker to perform an attack. The attacker needs enough total hashing power to break the security of PoW. whereas the security of the Proof of Stake (PoS) consensus mechanism is associated with measurable rewards or penalties on the initial deposit and is mostly theoretically proven. Ethereum’s consensus transition from PoW to PoS brought many attack vectors to light, as described in the papers (Neuder et al. 2021) (D’Amato et al. 2022) (Schwarz-Schilling, Neu, et al. 2021). Most of these attack vectors are mitigated before the transition. However, many new attack vectors might be seen in upcoming upgrades. We employed agent-based modeling to model network behavior computationally to help with modeling the attacks and their mitigations. Our experiment imitates network behavior using a sleek and sublime representation of a participant in the Ethereum PoS consensus. We performed experiments to test the network behavior under various average information propagation delays and to test the network against an ex-ante reorg attack and its mitigation. The agent in our experiment can model network effects under different stake distributions between honest and byzantine, numerous network topologies, and a broad variety of information transmission latencies. This report goes into detail about some fascinating observations regarding the average block transmission, the ex-ante reorg attack mitigation impact on the block tree evolution under Gasper consensus, as well as the rate of adversary block finality and the success rate of the ex-ante reorg attack based on the block timeliness.

Ethereum in its current version reaches a maximum transaction throughput of 15 transactions a second and thus suffers from a scalability problem. The Polygon proof-of-stake blockchain presents itself as an already active solution to this problem. Previous research focuses on the fairness in other proof-of-stake blockchains and the scalability issue in general. Our contribution is to provide a careful investigation of incentives and decentralisation in Polygon PoS. To this end, we analyse the scalability potential by having a look at transactions, usage and distribution of rewards to participants in the network. Our results indicate that Polygon PoS, as a cheap solution, can enhance the transaction throughput. Furthermore, the blockchain has a fairly good user adoption paired with climate-friendliness. However, in order to be the ultimate scaling solution, there is the need to double-down on incentives and increase performance by a lot. It can also be shown that certain participants get disproportionately more rewards than others, as seen by applying measures like the Gini and Nakamoto index to the data. Centralisation seems to be a problem throughout the network. In other words, we find that Polygon PoS at the current stage is lacking incentives and decentralisation and only early adopters of the blockchain can profit from it.

This thesis presents the design of a decentralized reputation system situated within a data on-chaining solution built atop Substrate. In particular, it is a response to the quest for an ideal design of a reputation system for a blockchain oracle. We assume the context of a concrete instantiation of such an oracle, Acurast, and situate our reputation system therein. By means of extensively consulting the literature, frequently encountered threats to and vulnerabilities of present day reputation systems formed the basis for a requirements elicitation, along with technical demands stemming from the technical context in which the proposed solution is situated. Based on these requirements and building on well-established principles of the reputation system literature, we present a design and show how it satisfactorily addresses the previously defined requirements, related to security properties as well as performance measures. Our proposed reputation engine makes use of probability distributions to model reputation scores as the posteriori probabilities of binary events. We thus achieve a design that is highly performant, carries low memory overhead and is inherently immune to a number of well-known threats commonly mentioned in the literature. We thus put forward arguments for having come up with a quasi-ideal design, despite acknowledging that no IT system can be deemed perfect. Further, we show by means of agent-based modeling how a highly relevant expected failure rate within Acurast, can be reduced by almost half. Additionally, we show the reputation system’s simulated effect on the matching algorithm of Acurast.

In recent rapid expansion of retail investment in crypto assets and wide scale adoption of decentralized finance industry also known as DeFi there is a massive investment is being made in DeFi ecosystem. Many applications are being launched to target the retail investor, luring with higher APR and rewards. This has made DeFi an interesting avenue for investors. Most of the time this DeFi applications running on blockchain are viewed as democratization of the current finance system, but in recent time many have expressed concerns about unfair practices made my few powerful to manipulate the market. While DeFi is a step in the right direction, many have expressed concern over massive wealth accumulation by a few have the power to manipulate the prices. This thesis look at blockchain as a network of peers and tries to analyse the wealth concentration by various peers, specifically in Proof-of Stake protocol based blockchain Tezos and Casper. In the analysis, we have used various heuristics suggested in research papers on other blockchain and used similar heuristic to establish the centralization of wealth on PoS based chains as well. In our results, we have found that PoS based blockchain are suffering from concentration of wealth. However, the situation is much worse on PoW based blockchain like Bitcoin, Ethereum (analysis made previously before PoS transition) etc. We have concluded after performing large scale comparative analytics on weekly transactions and used statistical interpretation on graph networks to analyse different network properties. PoS based chains have managed to perform better in terms of rewards distribution for mining. More peers participation in block creations, but the transaction behaviour have shown as similar trend like PoW based blockchain.

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The world we know has changed over a brief period, with the ascent and spread of Covid-19. This affected the education sector resulting in offline classes to online classes. Technologies made it easy with numerous websites, materials, video lectures, courses, and techniques for the students. In this situation, the main problem arises with students not participating in the class having many reasons such as illness, being introverted, and feeling that they may be wrong. If we are not interested in something to talk about or are shy, we must face it so that we will make the best out of it. People remember the things they listen to carefully, so we can probably study less if we listen. In this research paper, we proposed a Blockchain based application, so students who are going to attend online classes would be able to participate more in the class. They will be able to gain incentives that are based on crypto currency and by using those cryptocurrencies they can spend it on fees or any other resources in the university which would be beneficial for students. They will be able to gain incentives that are based on cryptocurrency and by using those crypto currencies they can spend it on fees or any other resources in the university. We are using the most popular technology which is Blockchain technology to make sure that students who are attending online classes will be able to pay more attention. Also, we are using the most famous functionality of Blockchain which is incentivization. To give rewards to the students we will be using incentivization so they can pay more attention to the classes. This design is beneficial for teachers also to look at the status of each student and get in contact with them.

The introduction of blockchain-based cryptocurrency networks created new possibilities to analyze monetary transactions at a level of granularity and accessibility not present in any traditional financial system. Ethereum is currently the most prominent blockchain platform that supports smart contract functionalities, allowing third-party projects to build on their ecosystem. One such implementation is the creation of stablecoins; stablecoins have the purpose of acting as a bridge between fiat money and cryptocurrencies and are vastly used on lending and trading protocols in DeFi. However, recent events show that stablecoins are not always as stable as they claim. In this thesis, the structure of stablecoin economies is analyzed from a Micro Velocity, and wealth distribution perspective to study the level of (de)centralization and discuss the implications of the findings on different stablecoin designs. The results show that the Micro Velocity is very heterogeneously distributed across the token networks, with a high correlation between an agent's contribution to velocity and an agent's wealth. Further, it was found that most addresses responsible for the vast majority of Micro Velocity belong to financial intermediaries–indicating a trend towards centralization.

The analysis of Bitcoin blockchain data, especially the transaction data, is interesting for both blockchain and economic researchers, in specific behavioural economic researchers. The blockchain serves as an accounting system that is managed in a decentralised manner and involves many participants. Blockchain makes use of the distributed ledger technology (DLT) but goes further and is characterised by the following five features in particular: (i) Cryptography, (ii) peer-to-peer (“P2P”) network, (iii) Consensus Mechanism, (iv) ledger, and (v) the Validity Rules. In this respect, the blockchain facilitates the transfer of assets and data and does not require a trusted central authority. However, interest in blockchain technology was triggered or increased by the emergence of the cryptocurrency Bitcoin. The cryptocurrency Bitcoin enables the exchange of valuable goods and services without a central authority. Thus, it fulfils the function of cash. With its cryptographic structure, it enables the user to stay anonymous while doing transfers. Furthermore, the blockchain infrastructure offers the possibility of analysing data of economic transactions between users and thus recognising economic patterns. Previous research has focused on the pure financial or technical aspect of Bitcoin, but not on the structural properties of the network of Bitcoin users. Thus, the dynamics of the Bitcoin network remain unconsidered. However, research into the structural properties of the Bitcoin network can draw conclusions about how the behaviour of users can be influenced by other users. In this paper, this possibility is taken into account by examining the consequences of two major events, the bankruptcy of the crypto bank Mt. Gox in 2014 and the ongoing Covid-19 pandemic, respectively its emergency in March 2020. The comparison of these two events should contribute to a better understanding of the behaviour of Bitcoin users and the associated price formation mechanism.

The Proof-of-Stake consensus algorithm offers great advantages over Proof-of-Work in the area of power consumption. Despite its increasing importance, thorough studies of Proof-of-Stake blockchains regarding their decentralization and fairness are still rare. This work contributes to the existing body of research by examining CasperLabs' Proof-of-Stake blockchain using data analysis for measures of wealth distribution, fairness, and decentralization. Furthermore, a simulation is conducted that investigates whether specific properties of the Casper reward mechanism have an impact on wealth distribution. The data analysis results indicate that the Casper network exhibits high levels of centralization and inequality, but that there is an increasing tendency towards decentralization over time. The simulations indicate that the reward distribution mechanism leads to a slower concentration of wealth compared to common Proof-of-Stake blockchains.

Blockchain develops quickly in the past few years with lots of new consensus mechanisms and networks coming out. One of the most famous blockchains is Terra, utilizing Proof-of-Stake consensus model to realize the concept, stablecoin. Former studies concentrate more on the consensus mechanism and the success story. However, after May 2022, Terra got a severe attack and the whole network crashed afterward. Our research looks deeper into the irregularities and comprehensive measurement of the performance of the network, trying to reveal some key insights about Terra’s failure. The Gini index is used to analyze the past five-month network equality of validators rewards and delegators rewards respectively. Mining speed and rewards average distribution are also calculated for each month to show their equality. We also compare results between pre-attack and post-attack to see how the network changes after the huge crash. Through detailed and complete studies, we found some potential signals or reasons for the attack based on results that the network becomes more and more centralized and inflexbile, which may lead to unbalanced distribution and less motivation for users to participate. Therefore, we can reach the conclusion that Terra is becoming more unequal and bloated in the past five months before the attack.

The thesis uses bitcoin data to estimate parameters, with which it is then tried to assess whether the model proposed in Gabaix and Koijen (2021) can explain the mean and standard deviation of bitcoin returns. The model uses the price elasticity of demand to generate observed prices and is stated in perturbations around a baseline. The results suggest that the model does not account for the full picture of return patterns observed. The moments resulting from the simulation are far larger in absolute terms than the observed moments and the estimation results are hardly statistically signi cant. Albeit, a parameter of the model, the speed of mean reversion of flows, has not been estimated, a back-of-the-envelope calculation implies a negative and very large value to counter the effects of the estimation results. A sensible estimation of this parameter is beyond the scope of this thesis. Bitcoin is well suited for price elasticity estimations because it is only marginally influenced by supply shocks i.e., all the shocks can be ascribed to the demand side. Moreover, the full record of transactions is in theory available to the public. A wide range of studies point to the effects trading has on prices, yet only recently has literature emerged arguing for inelastic  financial markets. The model presented in Gabaix and Koijen (2021) uses flows to funds to drive asset prices away from their fundamental values. This mechanism influences the stochastic discount rate in the model. For the estimations, two datasets are used. One dubbed `daily data' is provided by Stütz et al. (2020).1 This dataset consists of bitcoin transaction data aggregated on a daily and an entity level, transactions of bitcoin holders with only one address are omitted. The second called `block-level data' has been downloaded through the GraphSense API (Haslhofer et al., 2021). This dataset has been aggregated on a block-level and contains all transactions. Both datasets entail information about on-chain transactions. With this information, the estimations are performed on the transformed data. The data used is stated in deviations from its rolling averages. The rolling averages serve as a baseline. The results of the estimations suggest a positively inelastic demand and contrarian investing agents. The simulations suggest that the model overpredicts the volatility in bitcoin markets and overstates the premium paid by investors, in absolute terms.

Cryptocurrencies are based on blockchain technology which is a continuously extending list of data records stored in individual blocks. The entire transaction history is accessible to everyone and allows analyzing the relationships between the transaction network and prices of the currency on exchange markets. In this thesis, the history of Dogecoin is described with a closer look on the most influential network changes implemented after the official launch in 2013. Further, the causal relationships of the network properties and its price dynamics is analyzed in order to obtain meaningful statistics. Time series analysis is used to describe the evolution of the network and the influence of the price or vice versa. Four different network representations over a time period of 1959 days are considered. By inducing an impulse on the network properties and the price the results show evidence for causal relationships between Dogecoin price movements and its transaction network. Different effects are observed for the different representations considered. In addition, the same analysis is performed for Bitcoin over the identical time period. Similarities between the two cryptocurrencies for the behaviour of users after a price increase is shown. However, differences in the response of the price after a stimulus on the network properties lead to different purposes of the currencies and the enthusiasm of different target groups.