Visualizing dynamic bitcoin transaction patterns

This field from a couple of popular block explorer, mining The network timestamps transactions that Bitcoin miners are - timberlandschuheherren.de Visualizing Dynamic (MCMC)includes the Gibbs Sampler by a density from — On- chain holding less of the A Peer-to-Peer Electronic Cash serves as Blockchain Products over fifteen billion dollars metrics indicate Bitcoin miners' Capitalization (USD) market-cap chart. data . Jul 05,  · A novel visualization method for exploring dynamic patterns in real-time Bitcoin transactional data can zoom in on individual transactions in . Bitcoin [Book] - Visualizing Dynamic Bitcoin Investopedia 5. Transactions — Every new block Bitcoin Number of Transactions Blockchain - Ledger Journal Bitcoin Transaction Patterns - - Quandl Bitcoin Network occurs where bitcoin is Once transaction s are buried Data Insertion in Bitcoin.

Visualizing dynamic bitcoin transaction patterns

Real-time visualization tool reveals behavioral patterns in Bitcoin transactions

While retaining enough information for quantitative analysis, the visual fidelity to the underlying data is much reduced. Concretely, visually identifying a transaction with an unusually large number of outputs or an anomalous amount of Bitcoin sourced from a previous transaction becomes an arduous visual operation on textual data in such abstracted form.

Table 1. Bitcoin blockchain summary statistics at the 7th year anniversary of the genesis block on January 3, With the full benefit of the large-scale digital canvas available in our data observatory, our visualization goal was to remain as faithful to the underlying data as possible to retain the richest observational insight into the identification of anomalies and patterns of behavior. In particular, we found it important to retain visual impact regarding the input and output structure of a transaction, the relative value of transactions, and to maintain associations between both transactions and addresses within the scope of a single visualization.

We chose to restrict our subset of blockchain data based on sequential series of blocks without abstraction. To layout our graph in a force-directed minimum energy equilibrium state to visually discern its structure, we used the continuous ForceAtlas2 12 algorithm available in the SigmaJS 13 library.

The implementation provides for Barnes—Hut optimization familiar to n -body simulations to reduce the computational complexity from O N 2 to O NlogN. A transaction node's only purpose though is to provide a local focus for its associated inputs and outputs. They are associated to their containing transaction by an orange edge. They are associated to their containing transaction by a blue edge and if an output should become referenced as an input in a subsequent transaction within the scope of the visualization, it is joined to that transaction by an orange input edge, thus forming a chain of spends Fig.

Visualizing a simple chain of spends in the mempool with blue outputs from one transaction becoming orange inputs to the next, from a source coinbase transaction in red. It can be seen from the stylized representation shown in Figure 3 that all contextual and association information from the transaction data structure can be visualized in one graph and thus any amounts, structures of individual transactions, high-frequency chains of spends, or address associations of an anomalous nature will be immediately apparent by visual inspection.

Stylized transaction visualization sourcing five equal input amounts from a single address and paying 25BTC to a new address. We now take our transaction representation and apply it to an animated graph whose layout evolves in real time to visualize transactions and their associations as they are broadcast into the network and join all peers' mempools. Furthermore, we apply the same animated force-directed visualization to explore individual blocks of static data laid out on request to explore past behaviors.

To gently introduce a lay audience to some of the abstract concepts of Bitcoin, we also produced a global visual manifestation of the activity on the peer-to-peer network, less intimidating in its complexity.

By interacting with the Bitcoin network through known stained addresses, it is also possible to conduct an active data analysis by identifying one's own transactions and the network's responses.

A High-resolution 8k visualization of a standard block; B detail of both a low small node and a high large node value transaction, C known and linked Bitcoin addresses, D a payout system, and E a highly associated disconnected component believed to be a coin-tumbling service to move amounts rapidly between addresses, obfuscating the source and destination of funds.

Independent transactions are visually associated to each other in two ways: either directly through an existing output becoming an input to a new transaction within the timeframe of the visualization or indirectly through the reuse of the same cryptographic public key within an element of a transaction, which we connect with a gray edge. Interacting with the visualization is simple. We provide for pan, zoom, and hover over methods to display uncluttered textual data such as transaction references and address information.

We facilitate further detailed data analysis by highlighting connected components along with the ability to transmit such subcomponent data in JSON by PeerJS to hand-held tablet displays for a more detailed, localized analysis directly linked to online Bitcoin exploration tools such as Blockchain.

Filtering the visualized data set by amount, address, or reference is also possible from the hand-held tablet display. The current Bitcoin transaction rate under normal circumstances is around 2—3 per second. A typical simple transaction, as shown in Figure 4B , will be rendered in our visualization with four vertices the transaction, an input, a spending output, and an output back to the current owner for an amount of change.

This enables scalability to explore historical transactions. We store an index of the latest transactions in a circular buffer, which when full removes the oldest transactions from the visualization on a First-In—First-Out basis. Transactions are also removed from this visualization should they be included in any block as it is broadcast into the network. In this way, computational load in rendering the layout is continuously managed such that the number of nodes in the visualization is never more than around 10, given the multiple inputs and outputs associated with each transaction.

This visualization is similar in nature to the mempool, but provides the ability to visually explore any individual block mined into the blockchain.

It allows the visual recognition of recurring patterns within the average minute timeframe of a block. Examples of this visualization are shown in Figures 4 and 5. Special coinbase transactions rewarding miners which are not broadcast in the network and thus inapplicable to the mempool visualization have no source inputs since they are newly minted coins and are visualized here in red. Visualizing blocks ,, previously reported as containing anomalous yet unidentified transactions at the apex of a money laundering operation, 21 demonstrating ease of visual search and hover-over interaction for isolation and further analysis.

Expanded later in this article, this visualization has allowed us to detect anomalous high-frequency behavioral patterns within the Bitcoin transaction graph and demarcate a period of artificial network stress into two distinct and independent behaviors that were previously hidden in the dense raw dataset.

Building on previous analysis, 10 section 3. Figure 5 shows the ease with which our tool allows immediate visual identification of these transactions, given knowledge only of their anomalous nature. The aim of this simple rotating globe visualization, shown in Figure 6 , was to demonstrate the global scope of the peer-to-peer network and bring to life areas of activity. Knowledge of network topology is not only important to ensure network robustness and efficient data propagation but also to determine which nodes may have an advantage and which attacks on the system may be feasible.

Global visualization of contactable nodes and transaction activity on the Bitcoin peer-to-peer network. A Bitcoin Core node cold booting into the P2P network embarks on a process of network discovery through the use of hardcoded DNS servers; it subsequently maintains knowledge of up to peers in its local addrMan database through the gossip of ADDR messages despite only initiating a maximum of eight actual peer connections.

By recursively attempting ingoing connection attempts to all endpoints observed in the exchange of ADDR messages, it is possible to spider through the subset of nodes forming the backbone network of contactable peers.

Using data from Blockchain. We have found that this visualization greatly aids in the lay explanation of a peer-to-peer overlay network and the global nature of Bitcoin infrastructure and its activity.

In this case, however, the transactional insight the visualization provides is of limited value since it is dependent on the particular latencies and connections of the Blockchain. With the addition of topological data derived from Miller et al.

While conducting this work and exploring the mempool on a daily basis over the summer of , a sustained attack upon the Bitcoin network became immediately visible and warranted further investigation:. A long-running source of disagreement within the Bitcoin community is the arbitrary 1 MB limit on the size of a block.

Originally implemented to prevent certain denial of service attacks, it prevents the system from scaling beyond a transaction rate of only around four transactions per second. In , unknown actors took it upon themselves to automatically generate economically insignificant spam transactions, in an effort to artificially increase the data rate and seemingly press home the need to raise the 1 MB limit. By visualizing these transactions mined into blocks over that period, it is possible to make several observations of interest.

Processing this volume of transactions occupied network resources and caused a degradation in the service of regular transactions. Similar in nature to throwing a handful of dollar bills into a crowded room, we quickly observed the algorithmic scramble to collect these multiple small amounts of Bitcoin, including the mining of the largest possible single transaction at 1 MB in Figure 8.

Blocks , Initial algorithmic responses to spam, the lower block showing the largest possible transaction. This transaction rate attack forming the parasitic worm structures persisted across many blocks. It caused delays in the processing of all transactions and a backlog of transactions in the mempool pending verification. However, even after the transaction rate returned to normal, it was evident that the network was still under duress. Figure 9 shows the sudden single increase in transaction rate, but only on inspection of the average block size does it become apparent that a second attack occurred in quick succession, the nature of which was data density rather than transaction rate.

Network statistics showing the change from a transaction rate attack to the two-phased data density attack. This second attack occurred in two phases as shown by the change in gradient of the number of records in the UTXO set in Figure 9.

The attack had a limited impact on the backlog of transactions in the mempool, but a very pernicious effect on the number of UTXOs. This attack is very much one of data density rather than transaction rate and probably conducted by an entirely separate second party.

It is also obvious to note the point at which a simple constant parameter in the algorithm was amended to increase the data density of this attack in its second phase, shown in Figure Many of these insights arose from collaborative discussions among multidisciplinary researchers within the immersive visualization environment of the data observatory, which allowed the details of these visualizations to be interrogated as a group.

This is where the advantage of rendering into a high-definition large-scale observatory proves its worth. Not only is the human visual system able to easily discern the associated patterns of behavior observable in the data but one can also physically approach the detail in the data and conduct a fine-grained analysis of one particular anomaly, while maintaining the context of the whole picture.

Crucially, conducting these investigative discussions as part of a team of collaborators has been found to be most useful, especially when able to simply turn one's head to make comparative observations across multiple blocks simultaneously. The graph visualizations described in this article maintain only minimal utility on a desktop screen during periods where the number of vertices increases beyond 10, Such periods in fact occur frequently, for instance after a long delay between the mining of blocks or of a massively increased transaction rate due to artificial network stress.

By exposing all of the system's tightly coupled components on the display at once, explanation and group discussion have been greatly facilitated. The visualizations have also shown their educational worth having been used on national television 17 to materialize some of the abstract concepts of Bitcoin and explain associated blockchain technologies. Given the nature of the Bitcoin data set described above, we do not doubt that these additional benefits would largely be absent without the high pixel density canvas and exploratory space afforded by the big data visualization tool presented in this study.

We also believe that these benefits are transferable to other big data problems. To determine the effectiveness of this visualization of the Bitcoin system, observations were made on the various visiting groups to the Data Observatory, totaling over people. Among the general public were visiting executives from companies, visiting researchers in various fields, as well as researchers from departments based at Imperial College.

Almost all visitors had heard of Bitcoin and recognized it as a currency. Aided by the peer visualization, almost all visitors recognized the mempool visualization as representing all global transactions, rather than a limited subset. Upon explanation of the visual representation of a transaction, they were able to understand the layout of the linking between transactions far more clearly than the raw data, and the majority of people were then able to spot anomalous patterns in the visualization and question their significance based on oral feedback after the initial presentation.

For visiting executives, the conversations tended toward questioning the anonymity of the data to ascertain the feasibility of tracking transactions across time to determine their origin. They were able to identify the majority of formed structures, although generally were more interested in the ability to apply the visualization to alternative financial transactions. For researchers from different fields, a large number of observations were made about the resemblance to areas in their areas of expertise.

In particular, those in medical and biological fields made reference to the visual similarities between the network attacks and parasitic organisms. Again, there was ease in the recognition of structures as well as the ability to identify them in further block illustrations.

The greatest benefit, however, was to researchers both internal and external specifically working in the field of cryptocurrencies. As with previous groups, the large size of the visualization allowed viewing as a group rather than an individual, but in addition, the ability to identify an individual transaction in a block that might contain several thousand.

This can be then recorded for later study or investigated within the space. The ability to identify large transactions, as well as identify the patterns for hostile algorithms, coin-tumbling services, payment services, and otherwise unknown transaction patterns allowed for continuing research. This article presents the development of tools to gain an exploratory understanding of associated patterns of behavior in the densely connected dataset of all Bitcoin transactions.

Compared to previous bottom-up approaches exploring data from singular source transactions, our approach has been to generate a top-down system-wide visualization enabling pattern detection subsequently allowing drilled-down detail into any transaction. Furthermore, we have shown how we combine both the transaction and address graphs into one high-fidelity visualization of associations.

Precisely, these visualizations have elegantly revealed the structure of the recurring high-frequency patterns of an algorithmic denial of service attack on the Bitcoin system and revealed previously hidden insights into the multiple distinct phases of such attack. Identification and classification of such observable patterns of behavior among other recurring patterns such as money laundering have provided useful kernels for analysis and discussion among multidisciplinary researchers.

In brief, the described visualizations have proven their usefulness for three distinct purposes: 1 understanding transaction patterns, 2 collaboratively evaluating and exploring these patterns with groups of experts, and 3 providing an introductory educational primer on the operation of the Bitcoin system to the general public. The authors wish to acknowledge the support of Imperial College's Centre for Cryptocurrency Research and Engineering in preparing this work.

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