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Friedhelm Victor

Publications and source records attributed to Friedhelm Victor.

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Friend or Foe? Identifying Anomalous Peers in Moneros P2P Network

Monero, the leading privacy-focused cryptocurrency, relies on a peer-to-peer (P2P) network to propagate transactions and blocks. Growing evidence suggests that non-standard nodes exist in the network, posing as honest nodes but are perhaps intended for monitoring the network and spying on other nodes. However, our understanding of the detection and analysis of anomalous peer behavior remains limited. This paper presents a first comprehensive study of anomalous behavior in Monero's P2P network. To this end, we collected and analyzed over 240 hours of network traffic captured from five distinct vantage points worldwide. We further present a formal framework which allows us to analytically define and classify anomalous patterns in P2P cryptocurrency networks. Our detection methodology, implemented as an offline analysis, provides a foundation for real-time monitoring systems. Our analysis reveals the presence of non-standard peers in the network where approximately 14.74% (13.19%) of (reachable) peers in the network exhibit non-standard behavior. These peers exhibit distinct behavioral patterns that might suggest multiple concurrent attacks, pointing to substantial shortcomings in Monero's privacy guarantees and network decentralization. To support reproducibility and enable network operators to protect themselves, we release our examination pipeline to identify and block suspicious peers based on newly captured network traffic.

cs.NI

Moneros Decentralized P2P Exchanges: Functionality, Adoption, and Privacy Risks

Privacy-focused cryptocurrencies like Monero remain popular, despite increasing regulatory scrutiny that has led to their delisting from major centralized exchanges. The latter also explains the recent popularity of decentralized exchanges (DEXs) with no centralized ownership structures. These platforms typically leverage peer-to-peer (P2P) networks, promising secure and anonymous asset trading. However, questions of liability remain, and the academic literature lacks comprehensive insights into the functionality, trading activity, and privacy claims of these P2P platforms. In this paper, we provide an early systematization of the current landscape of decentralized peer-to-peer exchanges within the Monero ecosystem. We examine several recently developed DEX platforms, analyzing their popularity, functionality, architectural choices, and potential weaknesses. We further identify and report on a privacy vulnerability in the recently popularized Haveno exchange, demonstrating that certain Haveno trades could be detected, allowing transactions to be linked across the Monero and Bitcoin blockchains. We hope that our findings can nourish the discussion in the research community about more secure designs, and provide insights for regulators.

cs.CR

Monero Traceability Heuristics: Wallet Application Bugs and the Mordinal-P2Pool Perspective

Privacy-focused cryptoassets like Monero are intentionally difficult to trace. Over the years, several traceability heuristics have been proposed, most of which have been rendered ineffective with subsequent protocol upgrades. Between 2019 and 2023, Monero wallet application bugs "Differ By One" and "10 Block Decoy Bug" have been observed and identified and discussed in the Monero community. In addition, a decentralized mining pool named P2Pool has proliferated, and a controversial UTXO NFT imitation known as Mordinals has been tried for Monero. In this paper, we systematically describe the traceability heuristics that have emerged from these developments, and evaluate their quality based on ground truth, and through pairwise comparisons. We also explore the temporal perspective, and show which of these heuristics have been applicable over the past years, what fraction of decoys could be eliminated and what the remaining effective ring size is. Our findings illustrate that most of the heuristics have a high precision, that the "10 Block Decoy Bug" and the Coinbase decoy identification heuristics have had the most impact between 2019 and 2023, and that the former could be used to evaluate future heuristics, if they are also applicable during that time frame.

cs.CR

Disentangling Decentralized Finance (DeFi) Compositions

We present a measurement study on compositions of Decentralized Finance protocols, which aim to disrupt traditional finance and offer services on top of distributed ledgers, such as Ethereum. DeFi compositions may impact the development of ecosystem interoperability, are increasingly integrated with web technologies, and may introduce risks through complexity. Starting from a dataset of 23 labeled DeFi protocols and 10,663,881 associated Ethereum accounts, we study the interactions of protocols and associated smart contracts. From a network perspective, we find that decentralized exchanges and lending protocols have high degree and centrality values, that interactions among protocol nodes primarily occur in a strongly connected component, and that known community detection methods cannot disentangle DeFi protocols. Therefore, we propose an algorithm to decompose a protocol call into a nested set of building blocks that may be part of other DeFi protocols. With a ground truth dataset we have collected, we can demonstrate the algorithm's capability by finding that swaps are the most frequently used building blocks. As building blocks can be nested, i.e., contained in each other, we provide visualizations of composition trees for deeper inspections. We also present a broad picture of DeFi compositions by extracting and flattening the entire nested building block structure across multiple DeFi protocols. Finally, to demonstrate the practicality of our approach, we present a case study that is inspired by the recent collapse of the UST stablecoin in the Terra ecosystem. Under the hypothetical assumption that the stablecoin USD Tether would experience a similar fate, we study which building blocks and, thereby, DeFi protocols would be affected. Overall, our results and methods contribute to a better understanding of a new family of financial products.

cs.CR

Detecting and Quantifying Wash Trading on Decentralized Cryptocurrency Exchanges

Cryptoassets such as cryptocurrencies and tokens are increasingly traded on decentralized exchanges. The advantage for users is that the funds are not in custody of a centralized external entity. However, these exchanges are prone to manipulative behavior. In this paper, we illustrate how wash trading activity can be identified on two of the first popular limit order book-based decentralized exchanges on the Ethereum blockchain, IDEX and EtherDelta. We identify a lower bound of accounts and trading structures that meet the legal definitions of wash trading, discovering that they are responsible for a wash trading volume in equivalent of 159 million U.S. Dollars. While self-trades and two-account structures are predominant, complex forms also occur. We quantify these activities, finding that on both exchanges, more than 30\% of all traded tokens have been subject to wash trading activity. On EtherDelta, 10% of the tokens have almost exclusively been wash traded. All data is made available for future research. Our findings underpin the need for countermeasures that are applicable in decentralized systems.

cs.CR

Cross-Layer Deanonymization Methods in the Lightning Protocol

Bitcoin (BTC) pseudonyms (layer 1) can effectively be deanonymized using heuristic clustering techniques. However, while performing transactions off-chain (layer 2) in the Lightning Network (LN) seems to enhance privacy, a systematic analysis of the anonymity and privacy leakages due to the interaction between the two layers is missing. We present clustering heuristics that group BTC addresses, based on their interaction with the LN, as well as LN nodes, based on shared naming and hosting information. We also present linking heuristics that link 45.97% of all LN nodes to 29.61% BTC addresses interacting with the LN. These links allow us to attribute information (e.g., aliases, IP addresses) to 21.19% of the BTC addresses contributing to their deanonymization. Further, these deanonymization results suggest that the security and privacy of LN payments are weaker than commonly believed, with LN users being at the mercy of as few as five actors that control 36 nodes and over 33% of the total capacity. Overall, this is the first paper to present a method for linking LN nodes with BTC addresses across layers and to discuss privacy and security implications.

cs.CR

Blockchain-based Supply Chain Traceability: Token Recipes model Manufacturing Processes

Growing consumer awareness as well as manufacturers' internal quality requirements lead to novel demands on supply chain traceability. Existing centralized solutions suffer from isolated data storage and lacking trust when multiple parties are involved. Decentralized blockchain-based approaches attempt to overcome these shortcomings by creating digital representations of physical goods to facilitate tracking across multiple entities. However, they currently do not capture the transformation of goods in manufacturing processes. Therefore, the relation between ingredients and product is lost, limiting the ability to trace a product's provenance. We propose a blockchain-based supply chain traceability system using smart contracts. In such contracts, manufacturers define the composition of products in the form of recipes. Each ingredient of the recipe is a non-fungible token that corresponds to a batch of physical goods. When the recipe is applied, its ingredients are consumed and a new token is produced. This mechanism preserves the traceability of product transformations. The system is implemented for the Ethereum Virtual Machine and is applicable to any blockchain configuration that supports it. Our evaluation reveals that the gas costs scale linearly with the number of products considered in the system. This leads to the conclusion that the solution can handle complex use cases.

cs.CY