Alpaydin comments on the goodness of policies, which is determined by a sequence of good actions which attain a desired goal. Building on these learning techniques, the following literature looks at the analysis of Bitcoin networks using Machine Learning and Artificial Intelligence techniques with application to money laundering and fraud detection.
Yin and Vatrapu analyze the clusters, entities and categories that are used to understand the control over funds in the Bitcoin network along with attributing some form of contextualization to the clusters with respect to the activity they are performing e. Mining, mixing, exchanges. They also categorize based on criminal activity, in total the categories provided are Tor markets, scams, ransomware, mixing, and stolen bitcoins, exchange, gambling, merchant services, hosted wallets, mining pools, personal wallets.
A methodology is provided outlining the data required from each cluster for analysis. This data includes: Transactions hash, timestamp, input address, output address and value , addresses address, number of transactions with peer address and value , counterparties counterparty address, value, category and counterparty name , and exposure.
Exposure acts as a risk calculation based on the knowledge of the cluster in terms of how many inputs and outputs out of total transactions emanate or arrive at a particular service category. The pipeline and analysis process diagram summarize the methodology having a big emphasis on data collection, cleansing, preparation and feature extraction. This reflects the high level of effort required to get the data ready to analyze.
The second half of the diagram brings forth the machine learning capabilities for training data sets, model selection and validation. The statistical limitations on the machine learning components are identified in terms of the over and under sampling of the various classes, which limits the predictability of the under sampled classes.
However, this methodology is something that can be refined with improved data collection, training and classification. This may be able to improve the 0. Harlev et al. By looking at the anatomy of a Bitcoin cluster and using supervised machine learning to attribute Bitcoin clusters to those predetermined categories they break down the cluster structure to help categorize the controlling entities.
Clustering will only take the analysis so far and emerging techniques based on neural networks that apply deep learning of latent representations on a graph or network structure provide an advantage. They take this so far as saying it is an unviable approach which may only yield one bad transaction in more than a million. Therefore, there is a need to explore other machine learning methods to minimize the occurrence of the false positive and false negative detections and consequences of such detections.
Whilst Yin and Vatrapu used supervised learning techniques, Monamo et al. The k-means algorithm can perform clustering and classification without a training data set leaving the algorithm to establish its own labels as it comes across the data that is fed into it. This is both a limitation and a performance enhancement when it comes to fraud detection. Limitation in that unlabeled data somehow needs to be checked, modified and fed back into the system with context manually.
Performance enhancing as it will execute its machine components quicker. The authors concede that in the criminal detection process comparing known criminal elements would be better served using a neighborhood-based algorithm. These types of algorithms use classifiers to help the machine understand the context of the data they are processing and thus making the results more easily validated by experts in the field. They explain the open source nature of this algorithm and the previous application of the algorithm to web search results clustering by Osinski The unsupervised application of LSI discovers abstract context in the data that passes through it.
It forms cluster labels to be used as a reference for the supervised VSM algorithm. This is then used to determine cluster contents Osinski, Their results show a need to tune the algorithm with the input of subject matter expertise if any meaningful suspicious activity is to be found. Illicit money flows have traditionally been treated as anomaly detection problems. Researchers Graves and Clancy at DeepMind look to solve anomaly detection using unsupervised learning methods.
One such advanced method seeks to train an algorithm to generate its own models of the underlying classification of data it has discovered. Such techniques can only be enabled through deep learning which provides a deep understanding of the data being observed in its context. Steenfatt et al. An example given by Steenfatt et al. The labels identified one of three types of fraud and grouped the transactions accordingly. As an alternative to graph embedding, Li et al.
GNNs are used to learn unlabeled graph structures by using the underlying encoded graph structured data Zhang et al. Li et al. This technique is related to the field of ransomware and through the application of graphs formed by ransomware—Bitcoin transactions the literature shows it is possible to understand the similarities and differences in a ransomware target network model. In addition, by creating a GNN for ransomware—Bitcoin graphs it is possible to machine train and learn what behaviors and parameters these networks may form in the future.
The collaboration between cryptocurrency forensic analysis firm Elliptic and researchers at IBM and Massachusetts Institute of Technology MIT have released a public data set of around , transactions partially labeled with illicit or non-illicit flags to identify suspicious transactions on the blockchain within the context of Anti-money Laundering AML Weber et al.
Using graph analysis techniques such as Graph Convolutional Networks GCN which use neural networks to allow the embedding of relational information between nodes and relationships to be further used in machine learning techniques. A GCN aggregates the in and out degrees of a nodes neighbor and propagating these representations as features onto the nodes of the network. The DeepWalk embeds structural information on the graph to learn the typology of the graph by building up a node's context in the graph through a number of random walks from that node, much the same way a Natural Language Processing NLP algorithm learns words in a sentence from a corpus, or vocabulary, of words Perozzi et al.
In this research, GCNs are also used to predict super nodes, those nodes in a Bitcoin network having a large amount of incoming and outgoing edges, which could be indicators of ransomware addresses and activity on the Bitcoin network. The techniques for examining the Bitcoin blockchain as a graph require a combination of machine powered analytics combined with human subject matter expertise in order to contextualize the data for intelligence collection and forensic interpretation.
The ability to apply high performance computing to large amounts of data in the Bitcoin ecosystem provides efficiencies in analysis. Clustering data around influential nodes in the Bitcoin graph is a common approach undertaken by most of the authors of the literature. It allows for the application of graph algorithms relating to community detection, pageRank and centrality. Adding labels to the data collected and also combining the Bitcoin data with external data sources builds intelligence into the graph model by encoding structural knowledge into the graph such as in, out, or change addresses, timestamps, amount sent and received, service labels, network depth and address reuse frequency.
A recent example of this is the open data project by Michalski et al. They collected Bitcoin addresses and labeled them as mining pools, miners, coinjoin services, gambling services, exchanges, other services for training machine learning algorithms to learn and predict future addresses.
A targeted application of these techniques is to the case of identifying ransomware payments in Bitcoin. At present there is limited application in this realm, however the intention is to look for similar graph patterns across different ransomware campaigns. Future research will be able to build upon these techniques and apply deep learning and Artificial Intelligence AI to further enhance the ransomware-Bitcoin target network model with labeled data and augment the cognitive process for identifying ransomware networks in the Bitcoin ecosystem.
Ransomware is a prevailing threat to the mainstream usage of cryptocurrencies and for malware developers and users, cryptocurrencies have enabled cyber criminals to collect their proceeds of crime undetected. Since the estimated global damage of ransomware has increased 2. There is an essential need for identification and analysis frameworks.
Ahn et al. Using cluster analysis on the total network of the Cryptolocker ransomware campaign, they were able to understand the underlying financial infrastructures and money laundering strategies of the ransomware. It also speculated connections to criminal activity like the sheep marketplace, which was used for transacting narcotics, and was the successor to the infamous Silk Road site.
The methodology used by Ahn et al. At an individual transaction level, the framework followed the input and output addresses, bitcoins transferred, and timestamps of these transfers. These parameters were used to build the target network model for their research, along with additional labels to indicate the network depth i. Bistarelli et al. Through their analysis of the WannaCry attack, they were able to visualize the Bitcoin flows of WannaCry.
This revealed certain payments coming from leading crypto exchanges such as poloniex. It is important to take a full view of the continuum to build out the complete target network model, from mobilization through to actions on the objectives of the collected ransom. Furthermore, Paquet-Clouston et al. The authors investigate the graph formed by the incoming ransom payments and applied graph analysis techniques, such as centrality, to classify addresses to a particular ransomware.
The two ransomware campaigns examined in detail from a graph analysis perspective were Locky and CryptoHitman. Transaction walks were produced showing which nodes in the graph acted as collectors and what services the addresses corresponded to, i. A longitudinal time series analysis was also conducted which showed the profile of a ransomware address and how it collected ransoms over time. Many of these profiles were similar, i.
Performing the time series analysis looks back at the history of a particular collector address and this is also important to understand the behavior of the victims and attacker. Paquet-Clouston et al. Patterns are one structure of interest providing a footprint to ransomware-Bitcoin activity. Another is measuring the impact or significance the ransomware attack had by plotting their collection and payment profiles.
Conti et al. The paper focuses solely on the number of Bitcoins received by the ransomware Bitcoin addresses over the time window for the ransomware campaign. They also look at the cumulative distribution function CDF of the ransomware to show the total amount of ransom collected over the campaign. This is a relatively simplified analysis that provides an approach to deal with some blockchain specifics on multiple input transactions and change addresses.
Huang et al. The paper outlines a robust framework for identifying ransom addresses by scraping reports from real victims, creating synthetic victims under lab control conditions by making micropayments and tracing the flow of bitcoins and via clustering by co-spending which looks at addresses that create a transaction controlled by the ransom seed wallet. In addition, external data sources are looked at for information regarding the ransomware campaign.
Once this framework has been set up and the initial detection and collection has been done, payment analysis can be conducted to look at things like estimating revenue of the ransomware, payment mechanics timing and profile and potential cash-out behavior. Cash-out behavior is one of the more interesting parts of the ransomware—bitcoin analysis as it gives targeted evidence on criminal behavior relating to ransomware attackers looking to use their proceeds of crime.
The techniques used for ransomware—Bitcoin analysis vary across the intelligence-forensics continuum using the elements discussed and by adding data attributes to nodes and vertices in a graph by labeling, it is possible to aid graph classification using graph machine learning algorithms to find similarity or trends in the graphs Tiao et al. From the aforementioned literature, the importance of populating the target network model with context relevant data and comparing against different graphs from a variety of ransomware campaigns becomes evident.
However, for law enforcement agencies to benefit, it is imperative that law enforcement agencies, financial intelligence units and cryptocurrency service providers should cooperate and share information. There is precedent for this. This project supported forensic analyses relating to criminal transactions, anomaly detection and machine learning techniques which were developed as a solution for investigations relating to criminal and terrorist acts using cryptocurrencies on the internet.
Demonstrating a strong partnership between technology and subject matter experts, Titanium is model project from which law enforcement can build upon to strengthen their role alongside technology in the discovery and fight against illicit cryptocurrency usage.
This paper reviewed various techniques that are quite limited on their own. However, in combination these techniques are a formidable arsenal, much greater than the sum of the individual techniques. These techniques range from the simple heuristic approaches that help assume ownership of addresses and transactions, to the graph algorithms that provide essential foundations for community detection, PageRank and connectedness patterns in illicit networks.
Moreover, advanced computing power is enabling a resurgent field of Artificial Intelligence AI. Machine Learning, when applied to graphs and networks, produces rich contextual understanding of graph behavior and opens new horizons for anomaly detection. It facilitates very detailed and complex benchmarking and pattern detection. This automated simultaneous analysis lends itself well to the Bitcoin—blockchain environment as the graphs formed here are constantly being updated with new addresses and transactions.
This capability is particularly useful for ransomware attacks whose first indications are often sudden bursts of activity on the blockchain Bhatia et al. The literature reviewed in this paper forms a coherent approach to the analysis of the Bitcoin blockchain for illicit money flows. This approach revolves around techniques that seek to reduce the levels of anonymity provided by the Bitcoin system to identify real world participants.
The literature reveals challenges with the regulatory environment. The different applications of laws and compliance controls across jurisdictions can hinder deanonymization and attribution to the real world of virtual identities on the cryptocurrency network. The emergence of machine learning and its application to graphs is providing a powerful analysis capability for disrupting Bitcoin related criminal activity.
Particularly important are the practices of graph analysis, clustering, connectedness and GNNs as a form of deep learning applied to graphs. When compared to standard machine learning that employ supervised learning techniques and rules-based anomaly detection, these graph-based techniques dramatically enhance the future-orientated intelligence and real-time analysis of Bitcoin transactions.
Ultimately, the literature shows that there is no lack of available data on the Bitcoin blockchain. By providing open data this allows the community to flag certain behavior or orientation of Bitcoin addresses and transactions. However, the challenge is to correctly identify and classify the data and link it to off-chain data to provide a richer context. A way to potentially improve the performance of the machine learning algorithms is to take the graph labeling another step further.
This would require adding more meta-data to the graph that attributes the addresses and transactions to various classifications, such as ransomware or other illicit purposes. These challenges have precipitated open data efforts such as those conducted by joint research collaborations at Harvard dataverse Michalski et al.
AT: main author. SM and AU: corresponding authors, research supervisors, and editors. All authors contributed to the article and approved the submitted version. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. They allow for topological sorting which is an important property providing order to process each vertex before any of its successors Skiena, Ahn, G.
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Needham, M. Osinski, S. An algorithm for clustering of web search results Master thesis. Poznan: Poznan University of Technology. Paquet-Clouston, M. Perozzi, B. Pilarowski, G. Purplesec The Growing Threat Of Ransomware. PubMed Abstract Google Scholar. Reid, F. Richert, W. Birmingham: Packt Publishing. Ron, D. Rosenfeld, M. Analysis of bitcoin pooled mining reward systems.
Skiena, S. The Algorithm Design Manual. London: Springer-Verlag London. Spagnuolo, M. Steenfatt, N. Stokes, R. Virtual money laundering: the case of Bitcoin and the Linden dollar. Law 21, — The Law Library of Congress Regulation of Cryptocurrency in Selected Jurisdictions. We provide a great variety of mining components, including ASIC miners, GPUs, power units, and other parts, in addition to pre-assembled, ready-to-use mining rigs.
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