TRIDEnT: Building Decentralized Incentives for Collaborative Security

Sophisticated mass attacks, especially when exploiting zero-day vulnerabilities, have the potential to cause destructive damage to organizations and critical infrastructure. To timely detect and contain such attacks, collaboration among the defenders is critical. By correlating real-time detection information (alerts) from multiple sources (collaborative intrusion detection), defenders can detect attacks and take the appropriate defensive measures in time. However, although the technical tools to facilitate collaboration exist, real-world adoption of such collaborative security mechanisms is still underwhelming. This is largely due to a lack of trust and participation incentives for companies and organizations. This paper proposes TRIDEnT, a novel collaborative platform that aims to enable and incentivize parties to exchange network alert data, thus increasing their overall detection capabilities. TRIDEnT allows parties that may be in a competitive relationship, to selectively advertise, sell and acquire security alerts in the form of (near) real-time peer-to-peer streams. To validate the basic principles behind TRIDEnT, we present an intuitive game-theoretic model of alert sharing, that is of independent interest, and show that collaboration is bound to take place infinitely often. Furthermore, to demonstrate the feasibility of our approach, we instantiate our design in a decentralized manner using Ethereum smart contracts and provide a fully functional prototype.Comment: 28 page

Fairer, faster, more foreseeable: incentives, throughput and stability of proof of work blockchains

Blockchains employ internal and external incentive structures to influence participant behaviour, maintain network security, and ensure stable throughput. Internal incentives, like block rewards and transaction fees, are embedded within the blockchain design, while external incentives arise from market forces and competition. Both incentive structures are crucial for shaping blockchain behaviour and network efficiency. The primary motivation of this thesis is to examine how misaligned incentive structures can negatively affect stability in Proof-of-Work blockchains, focusing on stable block and transaction throughput. The thesis aims to provide novel insights into the negative impact of unstable throughput on individual agents and the network as a whole. Additionally, it evaluates potential attack vectors resulting from misconstructed incentive structures, past exploits, and proposes fairer and more robust mechanisms to align incentives, ensuring higher throughput stability and network security. The contributions of this thesis include the development of an open-source simulation framework called PoolSim. It enables the analysis of miner behaviour under different mining pool reward distribution schemes, including the profitability evaluation of queue-based manipulation strategies and pool-hopping between such pools. The thesis introduces the uncle traps attack, specific to Ethereum queue-based mining pools, which adversely affects block throughput and presents a fix to the uncle block reward distribution mechanism. Furthermore, this thesis examines the impact of difficulty adjustment algorithms on block throughput. It identifies instability in block solve times due to cyclicality observed in Bitcoin Cash and analyses how miners' behaviour contributes to this phenomenon. A novel difficulty algorithm based on a negative exponential filter is derived, eliminating oscillations and ensuring more stable block solve times. Lastly, the thesis addresses transaction throughput improvement by presenting a gas price prediction model for Ethereum. It combines deep-learning-based price forecasting with an urgency-based algorithm, optimising the trade-off between timely inclusion and transaction cost. Empirical analysis and real-world evaluation demonstrate significant cost savings with minimal delays compared to existing mechanisms.Open Acces

Diversification Across Mining Pools: Optimal Mining Strategies under PoW

Mining is a central operation of all proof-of-work (PoW) based cryptocurrencies. The vast majority of miners today participate in "mining pools" instead of "solo mining" in order to lower risk and achieve a more steady income. However, this rise of participation in mining pools negatively affects the decentralization levels of most cryptocurrencies. In this work, we look into mining pools from the point of view of a miner: We present an analytical model and implement a computational tool that allows miners to optimally distribute their computational power over multiple pools and PoW cryptocurrencies (i.e. build a mining portfolio), taking into account their risk aversion levels. Our tool allows miners to maximize their risk-adjusted earnings by diversifying across multiple mining pools which enhances PoW decentralization. Finally, we run an experiment in Bitcoin historical data and demonstrate that a miner diversifying over multiple pools, as instructed by our model/tool, receives a higher overall Sharpe ratio (i.e. average excess reward over its standard deviation/volatility).Comment: 13 pages, 16 figures. Presented at WEIS 201

Prediction of the transaction confirmation time in Ethereum Blockchain

La blockchain propose un système d'enregistrement décentralisé, immuable et transparent. Elle offre un réseau de nœuds sans entité de gouvernance centralisée, ce qui la rend "indéchiffrable" et donc plus sûr que le système d'enregistrement centralisé sur papier ou centralisé telles que les banques. L’approche traditionnelle basée sur l’enregistrement ne fonctionne pas bien avec les relations numériques où les données changent constamment. Contrairement aux canaux traditionnels, régis par des entités centralisées, blockchain offre à ses utilisateurs un certain niveau d'anonymat en leur permettant d'interagir sans divulguer leur identité personnelle et en leur permettant de gagner la confiance sans passer par une entité tierce. En raison des caractéristiques susmentionnées de la blockchain, de plus en plus d'utilisateurs dans le monde sont enclins à effectuer une transaction numérique via blockchain plutôt que par des canaux rudimentaires. Par conséquent, nous devons de toute urgence mieux comprendre comment ces opérations sont gérées par la blockchain et combien de temps cela prend à un nœud du réseau pour confirmer une transaction et l’ajouter au réseau de la blockchain. Dans cette thèse, nous visons à introduire une nouvelle approche qui permettrait d'estimer le temps il faudrait à un nœud de la blockchain Ethereum pour accepter et confirmer une transaction sur un bloc tout en utilisant l'apprentissage automatique. Nous explorons deux des approches les plus fondamentales de l’apprentissage automatique, soit la classification et la régression, afin de déterminer lequel des deux offrirait l’outil le plus efficace pour effectuer la prévision du temps de confirmation dans la blockchain Ethereum. Nous explorons le classificateur Naïve Bayes, le classificateur Random Forest et le classificateur Multilayer Perceptron pour l’approche de la classification. Comme la plupart des transactions sur Ethereum sont confirmées dans le délai de confirmation moyen (15 secondes) de deux confirmations de bloc, nous discutons également des moyens pour résoudre le problème asymétrique du jeu de données rencontré avec l’approche de la classification. Nous visons également à comparer la précision prédictive de deux modèles de régression d’apprentissage automatique, soit le Random Forest Regressor et le Multilayer Perceptron, par rapport à des modèles de régression statistique, précédemment proposés, avec un critère d’évaluation défini, afin de déterminer si l’apprentissage automatique offre un modèle prédictif plus précis que les modèles statistiques conventionnels.Blockchain offers a decentralized, immutable, transparent system of records. It offers a peer-to-peer network of nodes with no centralised governing entity making it ‘unhackable’ and therefore, more secure than the traditional paper based or centralised system of records like banks etc. While there are certain advantages to the paper based recording approach, it does not work well with digital relationships where the data is in constant flux. Unlike traditional channels, governed by centralized entities, blockchain offers its users a certain level of anonymity by providing capabilities to interact without disclosing their personal identities and allows them to build trust without a third-party governing entity. Due to the aforementioned characteristics of blockchain, more and more users around the globe are inclined towards making a digital transaction via blockchain than via rudimentary channels. Therefore, there is a dire need for us to gain insight on how these transactions are processed by the blockchain and how much time it may take for a peer to confirm a transaction and add it to the blockchain network. In this thesis, we aim to introduce a novel approach that would allow one to estimate the time (in block time or otherwise) it would take for Ethereum Blockchain to accept and confirm a transaction to a block using machine learning. We explore two of the most fundamental machine learning approaches, i.e., Classification and Regression in order to determine which of the two would be more accurate to make confirmation time prediction in the Ethereum blockchain. More specifically, we explore Naïve Bayes classifier, Random Forest classifier and Multilayer Perceptron classifier for the classification approach. Since most transactions in the network are confirmed well within the average confirmation time of two block confirmations or 15 seconds, we also discuss ways to tackle the skewed dataset problem encountered in case of the classification approach. We also aim to compare the predictive accuracy of two machine learning regression models- Random Forest Regressor and Multilayer Perceptron against previously proposed statistical regression models under a set evaluation criterion; the objective is to determine whether machine learning offers a more accurate predictive model than conventional statistical models

Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-based Distributed Deep Learning

One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and establishing the concept of "blockchain" as a distributed ledger. As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies. However, many of them share one common feature: they require proof-of-work to support the generation of blocks (mining) and, eventually, the generation of money. This proof-of-work scheme often consists in the resolution of a cryptography problem, most commonly breaking a hash value, which can only be achieved through brute-force. The main drawback of proof-of-work is that it requires ridiculously large amounts of energy which do not have any useful outcome beyond supporting the currency. In this paper, we present a theoretical proposal that introduces a proof-of-useful-work scheme to support a cryptocurrency running over a blockchain, which we named Coin.AI. In this system, the mining scheme requires training deep learning models, and a block is only mined when the performance of such model exceeds a threshold. The distributed system allows for nodes to verify the models delivered by miners in an easy way (certainly much more efficiently than the mining process itself), determining when a block is to be generated. Additionally, this paper presents a proof-of-storage scheme for rewarding users that provide storage for the deep learning models, as well as a theoretical dissertation on how the mechanics of the system could be articulated with the ultimate goal of democratizing access to artificial intelligence.Comment: 17 pages, 5 figure

Time to Bribe: Measuring Block Construction Market

With the emergence of Miner Extractable Value (MEV), block construction markets on blockchains have evolved into a competitive arena. Following Ethereum's transition from Proof of Work (PoW) to Proof of Stake (PoS), the Proposer Builder Separation (PBS) mechanism has emerged as the dominant force in the Ethereum block construction market. This paper presents an in-depth longitudinal study of the Ethereum block construction market, spanning from the introduction of PoS and PBS in September 2022 to May 2023. We analyze the market shares of builders and relays, their temporal changes, and the financial dynamics within the PBS system, including payments among builders and block proposers -- commonly referred to as bribes. We introduce an MEV-time law quantifying the expected MEV revenue wrt. the time elapsed since the last proposed block. We provide empirical evidence that moments of crisis (e.g. the FTX collapse, USDC stablecoin de-peg) coincide with significant spikes in MEV payments compared to the baseline. Despite the intention of the PBS architecture to enhance decentralization by separating actor roles, it remains unclear whether its design is optimal. Implicit trust assumptions and conflicts of interest may benefit particular parties and foster the need for vertical integration. MEV-Boost was explicitly designed to foster decentralization, causing the side effect of enabling risk-free sandwich extraction from unsuspecting users, potentially raising concerns for regulators

Blockchain technology for the construction industry

One of the challenges that the construction industry faces is the lack of trust between participants and information sharing processes. Blockchain is a disruptive and emerging technology that can be used to add immutability, trust and transparency to information. This dissertation proposes a platform that aims to mitigate the problem of information sharing in the construction industry using blockchain technology. The platform allows to keep an immutable record of file interactions between construction participants and simulate document signatures that can later be verified. A proof-of-concept was developed using the Ethereum network, which was also used to evaluate the gas price influence in the execution duration of the transaction and its cost. It is concluded that blockchain technology can support information sharing in the construction industry.Um dos desafios que a indústria da construção enfrenta é a falta de confiança entre os intervenientes e os sistemas de partilha de informação. Blockchain é uma tecnologia disruptiva e emergente que pode ser usada para adicionar imutabilidade, confiança e transparência à informação. A presente dissertação propõe uma plataforma que pretende mitigar o problema de partilha de informação na indústria da construção utilizando a tecnologia blockchain. A plataforma permite manter um registo imutável das alterações efetuadas em ficheiros partilhados entre os vários intervenientes da obra e simular assinaturas de documentos que possam ser, posteriormente, verificadas. Foi desenvolvida uma prova de conceito utilizando a rede Ethereum sendo, de seguida, utilizada para avaliar a influência do preço unitário do gas na duração de execução da transação e o seu custo. Conclui-se que a tecnologia blockchain pode auxiliar a partilha de informação na indústria da construção