Fig 3 - uploaded by Michele Spagnuolo
Content may be subject to copyright.
Connection between DPR's address and a 111,114 BTC address  

Connection between DPR's address and a 111,114 BTC address  

Source publication
Conference Paper
Full-text available
Bitcoin, the famous peer-to-peer, decentralized electronic currency system, allows users to benefit from pseudonymity, by generating an arbitrary number of aliases (or addresses) to move funds. However, the complete history of all transactions ever performed, called “blockchain”, is public and replicated on each node. The data it contains is diffic...

Context in source publication

Context 1
... running BitIodine on these data points, we found that Ulbricht's known address belongs to a cluster of 6 addresses, all empty. Thanks to our path finders in the Exporter module, we automatically found a connection between the leaked address and a very wealthy address, 1933phfhK3ZgFQNLGSDXvqCn32k2buXY8a, as shown in Fig. 3. The chain is particularly interesting because every address appears in the blockchain with its first input coming from the previous one in the chain, and often addresses spend all their inputs to addresses on the right exclusively. In our opinion, this is a manual, rudimentary mixer or tumbler, and BitIodine found a meaningful ...

Citations

... Various works have effectively targeted the confidentiality of Bitcoin addresses by examining blockchain transactions [48][49][50][51][52]. By merging the benefits of ECDSA [35] and cryptography into a single system, we have proposed in this study the Neural Fairness Protocol, an innovative consensus process that greatly outperforms prior work. ...
Article
Full-text available
To protect participants' confidentiality, blockchains can be outfitted with anonymization methods. Observations of the underlying network traffic can identify the author of a transaction request, although these mechanisms often only consider the abstraction layer of blockchains. Previous systems either give topological confidentiality that may be compromised by an attacker in control of a large number of nodes, or provide strong cryptographic confidentiality but are so inefficient as to be practically unusable. In addition, there is no flexible mechanism to swap confidentiality for efficiency in order to accommodate practical demands. We propose a novel approach, the neural fairness protocol, which is a blockchain-based distributed ledger secured using neural networks and machine learning algorithms, enabling permissionless participation in the process of transition validation while concurrently providing strong assurance about the correct functioning of the entire network. Using cryptography and a custom implementation of elliptic curves, the protocol is designed to ensure the confidentiality of each transaction phase and peer-to-peer data exchange.
... On the other hand, it also exposes the problems of anonymous Bitcoin system mechanisms. Researchers have used these heuristics and off-chain information to develop blockchain analysis software, such as Bitlodine [15], BitConeview [16], Bit-Conduite [17] and BitExtract [18]. In this study, we aimed to cluster the addresses using heuristic algorithms comprehensively based on the previous work, and to further explore the potential relationships between entities using community detection algorithm. ...
Article
Full-text available
Single heuristic method and incomplete heuristic conditions were difficult to cluster a large number of addresses comprehensively and accurately. Therefore, this paper analysed the associations between Bitcoin transactions and addresses and used six heuristic conditions to cluster addresses and entities. We proposed an improved change address detection algorithm and compared it with the original change address algorithm to prove the effectiveness of the improved algorithm. By adding conditional constraints, the identified change address was more accurate, and the convergence speed of the algorithm was accelerated. Our work presented the pseudo‐anonymity mechanism of the Bitcoin system, which could be used by the law enforcement agencies to track and crack down illegal transactions.
... Cryptocurrencies such as Bitcoin are attractive for cybercriminals due to their decentralized nature, (pseudo-)anonymity, irreversible transactions, and the ease to buy and sell them. However, some cryptocurrencies such as Bitcoin have a public transaction ledger, which has enabled the analysis of diverse cybercrime activities such as ransomware [28,38,46,55,57,63], thefts [51], scams [22,51,56], human trafficking [58], hidden marketplaces [25,45,60], money laundering [52], and cryptojacking [65]. ...
Preprint
Cybercriminals often leverage Bitcoin for their illicit activities. In this work, we propose back-and-forth exploration, a novel automated Bitcoin transaction tracing technique to identify cybercrime financial relationships. Given seed addresses belonging to a cybercrime campaign, it outputs a transaction graph, and identifies paths corresponding to relationships between the campaign under study and external services and other cybercrime campaigns. Back-and-forth exploration provides two key contributions. First, it explores both forward and backwards, instead of only forward as done by prior work, enabling the discovery of relationships that cannot be found by only exploring forward (e.g., deposits from clients of a mixer). Second, it prevents graph explosion by combining a tagging database with a machine learning classifier for identifying addresses belonging to exchanges. We evaluate back-and-forth exploration on 30 malware families. We build oracles for 4 families using Bitcoin for C&C and use them to demonstrate that back-and-forth exploration identifies 13 C&C signaling addresses missed by prior work, 8 of which are fundamentally missed by forward-only explorations. Our approach uncovers a wealth of services used by the malware including 44 exchanges, 11 gambling sites, 5 payment service providers, 4 underground markets, 4 mining pools, and 2 mixers. In 4 families, the relations include new attribution points missed by forward-only explorations. It also identifies relationships between the malware families and other cybercrime campaigns, highlighting how some malware operators participate in a variety of cybercriminal activities.
... It has been demonstrated by now, however, that this use of pseudonyms does not make Bitcoin anonymous. This has in large part been driven by the development of various clustering heuristics that identify multiple pseudonyms operated by the same entity [2,10,14,31,[44][45][46], with research also showing that de-anonymization is possible at the network layer [5,25]. These clustering heuristics use patterns of usage present in the Bitcoin blockchain as evidence of the shared ownership of the pseudonyms they cluster together; one heuristic that has been particularly widely adopted is the so-called co-spend heuristic, which says that all addresses used as input to the same transaction belong to the same entity. ...
... The ability to cluster together the addresses used as an input to a transaction was first observed in the original Bitcoin whitepaper [35], and has been used in many subsequent works [2,31,[44][45][46]. Beyond this co-spend heuristic, researchers have developed other heuristics for clustering together Bitcoin addresses. ...
... A valid Bitcoin transaction needs to be signed using the private keys associated with all its inputs. This has given rise to a common heuristic for clustering together Bitcoin addresses, known as the multi-input or co-spend heuristic [2,31,[44][45][46]. This heuristic states that all inputs to a transaction are controlled by the same entity, using the fact that they have all signed the transaction as evidence of shared ownership. ...
Preprint
Full-text available
One of the defining features of Bitcoin and the thousands of cryptocurrencies that have been derived from it is a globally visible transaction ledger. While Bitcoin uses pseudonyms as a way to hide the identity of its participants, a long line of research has demonstrated that Bitcoin is not anonymous. This has been perhaps best exemplified by the development of clustering heuristics, which have in turn given rise to the ability to track the flow of bitcoins as they are sent from one entity to another. In this paper, we design a new heuristic that is designed to track a certain type of flow, called a peel chain, that represents many transactions performed by the same entity; in doing this, we implicitly cluster these transactions and their associated pseudonyms together. We then use this heuristic to both validate and expand the results of existing clustering heuristics. We also develop a machine learning-based validation method and, using a ground-truth dataset, evaluate all our approaches and compare them with the state of the art. Ultimately, our goal is to not only enable more powerful tracking techniques but also call attention to the limits of anonymity in these systems.
... Experience with Bitcoin, once cherished as the privacypreserving alternative to other digital payment methods, has demonstrated that it effectively only provides very weak pseudonymity for transactions [2], [8]- [10], [14], [15], [17]. Privacy-Preserving Payment (P3) systems have thus become the focus of interest, as the fundamental requirement for anonymous payments has not lost its relevance. ...
Preprint
Unlike suggested during their early years of existence, Bitcoin and similar cryptocurrencies in fact offer significantly less privacy as compared to traditional banking. A myriad of privacy-enhancing extensions to those cryptocurrencies as well as several clean-slate privacy-protecting cryptocurrencies have been proposed in turn. To convey a better understanding of the protection of popular design decisions, we investigate expected anonymity set sizes in an initial simulation study. The large variation of expected transaction values yields soberingly small effective anonymity sets for protocols that leak transaction values. We hence examine the effect of preliminary, intuitive strategies for merging groups of payments into larger anonymity sets, for instance by choosing from pre-specified value classes. The results hold promise, as they indeed induce larger anonymity sets at comparatively low cost, depending on the corresponding strategy
... Ransomware [9][10][11] and money laundering (ML) [12,13] are other common examples of unlawful activities often using cryptocurrencies. Ransomware is a form of malware that locks and encrypts a victim's files until a ransom is paid [9]. ...
Article
Full-text available
The “Bitcoin Generator Scam” (BGS) is a cyberattack in which scammers promise to provide victims with free cryptocurrencies in exchange for a small mining fee. In this paper, we present a data-driven system to detect, track, and analyze the BGS. It works as follows: we first formulate search queries related to BGS and use search engines to find potential instances of the scam. We then use a crawler to access these pages, and a classifier to differentiate actual scam instances from benign pages. Last, we automatically monitor the BGS instances to extract the cryptocurrency addresses used in the scam. A unique feature of our system is that it proactively searches for, and detects, the scam pages. Thus, we can find addresses that have not yet received any transactions. Our data collection project spanned 16 months, from November-2019 to February-2021. We uncovered more than 8,000 cryptocurrency addresses directly associated with the scam, hosted on over 1,000 domains. Overall, these addresses have received around 8.7 million USD, with an average of 49.24 USD per transaction. Over 70% of the active addresses that we are capturing are detected before they receive any transactions, that is, before anyone is victimized. We also present some post-processing analysis of the dataset that we have captured to aggregate attacks that can be reasonably confidently linked to the same attacker or group. Our system is one of the first academic feeds to the APWG eCrime Exchange database. It has been actively and automatically feeding the database since November-2020.
... The Bitcoin analytics in the paper are very short and the authors make no attempts to trace the illicit funds, however this is acknowledged as not being the primary focus. In 2014, Spagnuolo et al. [136] used their Bitcoin tracing, classification, and verification system to analyse crime and perform an analysis of potential connections between the Silk Road cold wallet and its founder, and measured on-chain crime from the CryptoLocker ransomware. ...
... Indeed, a long line of research [8,95,122,124,136] has by now demonstrated that the use of pseudonymous addresses in Bitcoin does not provide any meaningful level of anonymity. Beyond academic research, companies now provide analysis of the Bitcoin blockchain as a business [47]. ...
Preprint
Full-text available
This thesis presents techniques to investigate transactions in uncharted cryptocurrencies and services. Cryptocurrencies are used to securely send payments online. Payments via the first cryptocurrency, Bitcoin, use pseudonymous addresses that have limited privacy and anonymity guarantees. Research has shown that this pseudonymity can be broken, allowing users to be tracked using clustering and tagging heuristics. Such tracking allows crimes to be investigated. If a user has coins stolen, investigators can track addresses to identify the destination of the coins. This, combined with an explosion in the popularity of blockchain, has led to a vast increase in new coins and services. These offer new features ranging from coins focused on increased anonymity to scams shrouded as smart contracts. In this study, we investigated the extent to which transaction privacy has improved and whether users can still be tracked in these new ecosystems. We began by analysing the privacy-focused coin Zcash, a Bitcoin-forked cryptocurrency, that is considered to have strong anonymity properties due to its background in cryptographic research. We revealed that the user anonymity set can be considerably reduced using heuristics based on usage patterns. Next, we analysed cross-chain transactions collected from the exchange ShapeShift, revealing that users can be tracked as they move across different ledgers. Finally, we present a measurement study on the smart-contract pyramid scheme Forsage, a scam that cycled $267 million USD (of Ethereum) within its first year, showing that at least 88% of the participants in the scheme suffered a loss. The significance of this study is the revelation that users can be tracked in newer cryptocurrencies and services by using our new heuristics, which informs those conducting investigations and developing these technologies.
... Using an open-source framework, the Bitcoin blockchain was processed; public keys were clustered; clusters were tagged, and the network was shown. The system was able to identify an address holding 111,114 BTC pertaining to a Silk Road cold wallet and precisely estimate ransoms delivered to CryptoLocker using only an address provided by a victim on a forum as a lead [6]. Another technique was to use statistical analysis to figure out how its users behaved when sending, receiving, and keeping money. ...
Chapter
Full-text available
Bitcoin is a decentralized, pseudonymous cryptocurrency that has become one among the most demanded digital assets to date. Because of its uncontrolled nature and users’ inherent anonymity, it has seen a significant surge in its use for illegal operations. As a result, numerous systems for characterizing diversified entities across the Bitcoin network must be developed. In this work, we offer a way for breaking Bitcoin anonymity using a revolutionary cascade machine learning model that only utilizes a few features taken straight from Bitcoin blockchain data. We gathered approximately 29 million samples from diverse sources and generated data for four different entities: exchanges, gambling, pools, and services. On a dataset balanced using SMOTE and weight of the entities, the back-propagation neural network (BPNN) model was trained and tested. Cross-validation accuracy has been utilized to evaluate the model’s accuracy. On the dataset balanced using the weight of the entities, the BPNN model classified the entities with 71.51%, while with SMOTE, the accuracy of classification is 71.22%
... There has been a lot of work on address clustering [2,11,12,20,22,26,27,32,37]. All of these studies mentioned or used the common-input-ownership heuristic. ...
Article
Full-text available
Coin mixing is a class of techniques used to enhance Bitcoin transaction privacy, and those well-performing coin mixing algorithms can effectively prevent most transaction analysis attacks. Based on this premise, to have a well-functioning transaction analysis algorithm requires coin mixing detection with a high recall to ensure accuracy. Most practical coin mixing algorithms do not change the Bitcoin protocol. Therefore, the transactions they generate are not fundamentally different from regular transactions. Existing coin mixing detection methods are commonly rule-based that can only identify coin mixing classes with well-defined patterns, which leads to an overall low recall rate. Multiple rules could improve the recall in this situation, yet they are ineffective for new classes and classes with ambiguous patterns. This paper considers coin mixing detection as a transaction classification problem and proposes an LSTM Transaction Tree Classifier (LSTM-TC) solution, which includes feature extraction and classification of Bitcoin transactions based on deep learning. We also build a dataset to validate our solution. Experiments show that our approach has better performance and the potential for discovering new classes of coin mixing transactions than rule-based approaches and graph neural network-based Bitcoin transaction classification algorithms.
... If one can link a Bitcoin address to an identity, much private information such as transaction amount, parties, and time are disclosed. Some commercial software such as BitIodine [3] and BitCone-View [4] provide blockchain analysis services. Therefore, these privacy issues need to be addressed in order for a successful cryptocurrency deployment. ...
... Multi parties coin mixing is a well-studied topic and beyond the discussion of this paper. Users can use any of the proposed algorithms [3,4,5,6,7,8,9] for the initial mixing process so that nobody knew the mixing detail. ...