Discussion Paper Series 

Discover our latest research on detecting pump-and-dump schemes in decentralised finance using PUMPWATCHER, a novel framework combining graph neural networks and contrastive learning. Analysing over 924,000 Uniswap transactions from December 2022, the study demonstrates PUMPWATCHER’s ability to identify market manipulation with 92.3% balanced accuracy, significantly reducing false detections.

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The findings highlight a breakthrough in real-time fraud detection, paving the way for more secure and trustworthy DeFi ecosystems.

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Discover our latest paper examining consensus power inequality across five leading blockchain networks: Bitcoin, Ethereum, Cardano, Hedera, and Algorand. Drawing on data from January 2022 to July 2024 and using established economic metrics, the study reveals unexpected disparities in decentralisation. Notably, Ethereum shows increased concentration after moving to Proof-of-Stake.

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The paper offers a robust framework for assessing power distribution and provides insights into designing fairer, more resilient systems. Read the full study for a deeper understanding of blockchain power dynamics.

This paper analyses NFT liquidity on Hedera and Ethereum using on-chain data, introducing multiple dimensions for measuring token liquidity and comparing market concentration across both networks. It highlights key differences in how NFTs are created and circulated, offering a tentative exploration of the underlying factors driving these patterns. 

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The findings provide insight into the structural and behavioural distinctions between Hedera and Ethereum’s NFT ecosystems.

Logs are essential for recording system runtime information and are widely used for effective service management. However, traditional log parsing methods struggle with scale, leading to inefficiencies and reduced model accuracy. 

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This paper presents Aclog, an automated tool designed for large-scale log analysis, storage, and auditing. Aclog structures unstructured logs into a unified format. Its effectiveness is demonstrated using a large-scale public log dataset from ten widely used systems.

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Sharding is emerging as a key solution for blockchain scalability, but how can it be applied to Hedera’s unique architecture? This paper examines academic and industrial approaches before proposing a hybrid sharding model tailored to Hedera’s Gossip-about-Gossip and ABFT protocols. 

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The findings highlight reduced storage and communication overhead, improved scalability, and enhanced fault tolerance, demonstrating the viability of integrating sharding into Hedera’s distributed ledger infrastructure.

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Blockchain sharding promised to solve scalability but created critical challenges, fragmented networks struggling with dynamic workloads and vulnerable cross-shard transactions that compromise system integrity. DynaShard is the proposed solution, combining adaptive shard management with hybrid consensus to finally deliver on sharding's potential.

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This approach slashes latency by 42.6% whilst dramatically improving shard utilisation by 78.77% under extreme transaction volumes. DynaShard's innovative state synchronisation and dispute resolution protocols transform complex cross-shard interactions into seamless, secure operations, positioning it as the definitive framework for next-generation blockchain systems.

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Bitcoin's hidden weakness lies in natural forking, computational chaos wasting resources and threatening security. This research cracks the mathematical code behind fork formation, revealing how mining consolidation has secretly transformed Bitcoin over the past decade.

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The study unveils a predictive model exposing the balance between miner competition and network speeds, discovering pools have shrunk threefold with "rich-get-richer" dynamics. It demonstrates how hash rate consolidation paradoxically strengthens Bitcoin, providing architects tools for predicting network fractures determining decentralised mining's future.

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Flash loans have weaponised DeFi oracle manipulation, turning small price discrepancies into million-pound heists that drain lending protocols in seconds. This research uncovers vulnerabilities in Protocols for Loanable Funds and introduces SecPLF, a novel defence that turns attackers' speed against them. 

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SecPLF tracks cryptoasset price states and timestamps, creating dynamic price boundaries that block manipulation while preserving legitimate operations, neutralising arbitrage exploits and offering DeFi its first oracle-agnostic, lightweight protection.

Total value locked is DeFi's gold standard metric, but it's fundamentally flawed due to ‘double counting’ from wrapping and leveraging activities. This research introduces ‘total value redeemable,’ a new framework revealing the true underlying value of DeFi systems. The analysis uncovers that TVL exceeded actual value by $139.87 billion at DeFi's peak, with a TVL-to-TVR ratio of approximately 2.

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Using a novel DeFi multiplier mirroring traditional finance's money multiplier, the study demonstrates that a 25% ETH price decline triggers $1 billion greater non-linear decreases in TVL versus TVR through derivative token liquidations.

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Blockchain wallets face a fundamental trade-off: non-custodial wallets offer control but introduce security risks, whilst custodial wallets become primary attack targets by pooling funds. This research proposes a chain-agnostic Multi-Party Computation Threshold Signature Scheme shared-custodial wallet with securely distributed key management and recovery. 

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Applied to wealth management, the solution consolidates multiple cryptocurrency tokens and services into a single interface, addressing performance, privacy, and cost issues of traditional approaches.

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Fine-grained traffic analysis represents an advanced evolution beyond traditional traffic analysis, using sophisticated data mining techniques like deep learning to deduce application-layer activities, user behaviours, and even message content despite encryption. This survey examines frontier developments in FGTA, which has become essential for network administrators and attackers seeking network visibility. 

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The research investigates different FGTA approaches across critical applications including intrusion detection, website fingerprinting, location estimation, and user activity inference, whilst elaborating on operating principles, limitations, countermeasures, and promising future research avenues.

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Proof-of-Stake's ‘plutocratic’ nature requires intricate economic design to ensure resilience and safety, yet no comprehensive framework exists to analyse these complex interactions. This research establishes a generalizable, transferable PoS framework encompassing protocol reserves, treasury, reward pot, transaction fees, and staking rewards. 

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Applied to major protocols Hedera and Cardano, the dynamic model reveals protocol trajectories including treasury evolution, reserve balances, and decentralisation levels. The framework serves as a handbook for investigating staking behaviours and reward dynamics across various PoS-based systems.

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Governments worldwide are implementing Bitcoin mining bans to reduce environmental impact, but these well-intentioned policies may be counterproductive. This research quantifies the environmental effectiveness of mining bans by tracking displaced operations and their carbon emissions. 

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The findings reveal that Bitcoin mining bans in low-emission countries result in a net increase in global carbon emissions through aggravated carbon leakage, as operations migrate to higher carbon intensity regions. The study provides data-driven evidence for why more nuanced regulatory approaches are required for sustainable cryptocurrency governance.

Current Metaverse research focuses on environmental impacts without addressing practical solutions or social implications like digital divides and inequalities. This integrative literature review identifies five critical areas where the Metaverse will significantly impact environmental sustainability: energy consumption, e-waste, rare earth metals and conflict minerals, changes in physical travel, and encouraging sustainable behaviours. 

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The research reveals how renewable resources, recycling initiatives, social inclusivity, and cross-sector collaboration can mitigate negative impacts whilst ensuring the Metaverse offers equal opportunities across different populations.

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The Silicon Valley Bank collapse revealed counterintuitive dynamics in stablecoin markets regarding transparency effects. This research analyses USDC and USDT liquidity pools on Uniswap during the SVB crisis, adapting the Marginal Cost of Immediacy measure for automated market makers and conducting Difference-in-Differences analysis. 

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The findings reveal that USDC's frequent and detailed disclosures led to swift market reactions, whilst USDT's opacity and less frequent reporting provided a safety net against immediate impacts, demonstrating flight-to-safety behaviour where transparency paradoxically increased vulnerability.

IOTA's auto-peering algorithm builds peer-to-peer connections based on the ‘Mana’ endowment, but this reputation-based approach may contain critical vulnerabilities. This research interprets IOTA's auto-peering algorithm as a random network formation model and employs network metrics to identify cost-efficient attack strategies. 

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The analysis reveals how attackers can eclipse significant portions of the IOTA network, providing detailed estimates of attack costs and potential damage whilst characterising the unique properties of IOTA's homophile random networks.

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Ethereum-based decentralised exchanges suffer from high transaction costs and price synchronisation challenges that worsen during market volatility. This comparative study uses agent-based simulations with real market data to pit the XRP Ledger's protocol-level AMM-DEX against Ethereum's smart contract implementations like Uniswap V2. 

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Results show XRPL-AMM-DEX achieves superior price synchronisation, reduced slippage, and improved returns due to lower fees and shorter block times, with the integrated Continuous Auction Mechanism further mitigating impermanent loss for liquidity providers.

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Manual analysis of distributed ledger technology research has become impossible due to explosive publication growth. This study introduces an NLP-driven systematic literature review methodology that processes 24,539 publications using transformer-based language models fine-tuned for DLT and ESG domains. 

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The research narrows the selection to just 505 key publications, creating the first comprehensive literature review of DLT's environmental, social, and governance impacts alongside a unique dataset of 54,808 entities tailored for future research.

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Traditional DeFi governance relies on manual parameter adjustments prone to human bias and financial risks. This research introduces Auto.gov, a learning-based governance framework using deep Q-network reinforcement learning for semi-automated, data-driven parameter adjustments. 

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Testing on real-world data shows Auto.gov outperforms benchmark approaches by at least 14% and static baseline models by tenfold in protocol profitability, whilst demonstrating capability to retain funds that would otherwise be lost to price oracle attacks.

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As cryptocurrency adoption surges, wallet security incidents are becoming increasingly frequent and costly. This study introduces a multi-dimensional design taxonomy for cryptocurrency wallets and systematically analyses 84 real-world attacks between 2012 and 2024 that resulted in $5.4 billion in losses. 

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The research classifies existing industry wallets, identifies vulnerabilities, and presents a multi-layered attack framework alongside defence implementations, providing crucial insights for enhancing wallet security across the cryptocurrency ecosystem.

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Bitcoin mining has long been criticised for its energy consumption, but this systematic review challenges that narrative. This study analyses state-of-the-art research on Bitcoin mining and grid decarbonisation, exploring how proof of work mining could serve as a flexible load response to boost renewable energy penetration. 

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The findings suggest renewable-based mining could drive net-decarbonising effects, warranting fundamental re-evaluation of Bitcoin's environmental impact.

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Creating smart legal contracts currently requires teams of lawyers and programmers working together for months. This research introduces an automated pipeline using advanced Natural Language Processing (NLP) models to convert traditional legal documents into executable Smart Legal Contracts through the Accord Project framework. 

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The system achieves 80% accuracy in detecting legal components and successfully extracts one-third of Concerto variables automatically, complete with a user-friendly web interface that democratises smart contract creation.

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Are cryptocurrency miners playing by the rules, or are some cheating the system for profit? This study develops novel statistical methods to detect selfish mining attacks across Bitcoin, Litecoin, Monacoin, Ethereum and Bitcoin Cash. 

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The analysis uncovers the first empirical evidence of malicious mining behaviour in practice, including secret cartels in Monacoin where miners covertly share information to gain unfair advantages over honest participants.

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A decade after blockchain promised to eliminate centralised control, how decentralised are cryptocurrencies really? This comparative study applies complex systems methodologies across multiple blockchain networks to critically examine centralisation patterns in cryptocurrency systems. 

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The analysis reveals the stark gap between decentralisation ideology and technological reality, challenging fundamental assumptions about the current state of cryptocurrency systems.

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What connects 1970s accounting theory to today's cryptocurrency revolution? This genealogical study uncovers the hidden relationships between REA accounting, triple-entry accounting, and blockchain technology through exclusive interviews with field pioneers and comprehensive historical analysis. 

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This research paper reveals how three seemingly separate research streams converged to create the shared ledger explosion we see today, correcting decades of misconceptions about their interconnected evolution.

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Can blockchain data predict Bitcoin price movements? The study analyses nine years of Bitcoin transactions and 283 million trades across 16 million users to uncover hidden patterns between network activity and market volatility. 

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The findings reveal that user behaviour changes actually precede price drops, with the network showing increased fragmentation right before major downturns.

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