What are the different analysis techniques the Bitcoin miners need to know? Bitcoin, the original cryptocurrency, happens to be the most favored coin when it comes to crypto trading, investing or even crypto mining.
Therefore, it is needless to say that a lot of people are engaged in these activities which make it quite difficult to be successful.
If you are engaged in any such activities, especially Bitcoin mining, you will need to be very efficient to stay ahead of the competition and have that desired edge that will make you successful in your act.
You will therefore need to be well aware of what you are doing and what the returns you are getting and for that you will need to follow different analysis techniques.
If you are unaware of them, here is an article that will help you a lot in this matter.
The contents of this article will allow you to understand the mechanics of the system on the whole in a much better way and plan your moves accordingly.
This will ensure that you make substantial profits from your Bitcoin mining efforts.
Ideally, while Bitcoin mining you will need to interact with the Bitcoin protocol as well as with the internet security protocols while following the mining process just as you should all the time.
You will also need to make sure that your system is free from attacks and hacks by knowing the different attack vectors and the privacy motivations.
In order to help you with all these aspects, here are a few basic techniques for analyzing Bitcoin mining and transaction behavior.
What are the Different Analysis Techniques the Bitcoin Miners Need to Know?
Bitcoin mining as such is quite a difficult process which not only involves a lot of money but a lot of effort and time.
In order to be successful in this process, you will need to be efficient enough and have adequate computing power to solve the complex mathematical puzzles ahead of the others and win the reward.
This means that you should always be on your toes.
For that, apart from knowing the techniques to analyze your mining process and feasibility, you will also need to know about the Bitcoin transaction flow and its authenticity.
Though your main objective will not be identifying illegal transactions made on the blockchain it will surely help if you know about them to ensure your system is working just the way you want it to.
Since Bitcoin is the most favored crypto for laundering money and funding terrorism, the Bitcoin miners need to be very cautious while validating a transaction and approving it.
Everything should be in compliance with the AML and CTF laws and KYC provisions. Therefore, it is imperative that you will need to do a lot of analyses before you okay a transaction.
Remember, your analytical results may help further in forensic analyses if it is required to detect the source of any anomaly in the transactions.
Therefore, you should be well aware of the different technologies as well as analytical tools and techniques to validate transactions accurately.
These techniques include straightforward heuristic approaches to complicated graph algorithms and detection of addresses and transactions.
Knowledge of machine learning, deep learning and Artificial Intelligence all will be required to understand the graph and user behavior in a much better way.
The prime objective of the analyses is to reduce the anonymity levels in the Bitcoin system to discover real world transactions and participants but in keeping with the laws and compliance governance mechanisms across jurisdictions.
This will not affect the efficacy of the cryptocurrency network.
Since there is no lack of data on a Bitcoin blockchain you will not have any problem in using these techniques easily and validate Bitcoin transactions while mining.
For that, you will need to know a bit about Bitcoin heuristics to start with.
Bitcoin mining is all about dealing with information related to a transaction made on the blockchain network. This information can be a mosaic of different kinds that lets one know everything that is:
- Regulatory in nature and more.
This allows the miners to validate and confirm the authenticity of a particular transaction and add that information as a new block on the chain linked with the previous block with a hash.
When heuristics is introduced into the analysis process it will help you to identify the complexities of attribution and address them appropriately.
This can be easily achieved when you group transactions with the same type of behavior and link ownership to the service and addresses on the Bitcoin network.
This will help in revealing the identity of a participant, which, however, is against the anonymity aspect of crypto transactions.
Apart from that, adding heuristics to Bitcoin analysis also helps in following the concept of ‘peeling.’
This is the process wherein smaller transactions of Bitcoin are ‘peeled’ off larger amounts.
These transactions are then sent to another address. Anything that is left is sent back to the one-off change address.
Apart from that, this process also helps in identifying whether or not the user of the input address is also controlling the one-off change address that is linked to that particular transaction.
Here, it is assumed that a single user owns both the addresses.
Ideally, this is a very common process followed to confuse the movements of funds on the blockchain network. This helps to launder money on the Bitcoin network.
It also needs to make a lot of different time-series and Bitcoin service breakdown analyses to understand the effects of the different services on the network.
When you use the aggregated data found through these analyses you will be able to characterize different activities and trends on the Bitcoin network. If you dig deeper into the payment trends you will also be able to determine the ownership and the source as well.
These different data sources will help you in much better and advanced cluster analysis and you can also determine the patterns, structures, associations, and relationships as and when required.
This will help you to identify the common entities that may be controlling the Bitcoin addresses on the network.
Overall, using heuristics in your analysis will help you to know the transaction flows and have a better picture of it over time.
Bitcoin Network Analysis Techniques
Sometimes, you will also need to determine the openness of the Bitcoin network and the system.
For this you will analyze the Bitcoin network layer. This will help you to see well within some of the crucial features of the structure of Bitcoin transactions.
Some of the useful techniques for Bitcoin network analysis include a network traffic analyzer tool.
This tool will help you to analyze the Bitcoin protocol traffic and build a proper profile of the transaction flow between the Bitcoin addresses and IP addresses.
This process is called public key profiling. This will help a lot in your mining process over time.
When you know about the strengths and weaknesses in the Bitcoin network by analyzing in this technique you can adjust your Bitcoin mining activities accordingly and relate it to the graph data models.
It will also offer you some valuable insights about the relationships of the nodes and how these are formed to plan your future activities and ensure success in Bitcoin mining.
Graphics Analysis Techniques
You will also need to analyze a lot of graphs and charts off and on in order to ensure that your Bitcoin mining endeavor is heading the right way.
And, there are different graphics analysis techniques to follow depending on the type of graph in question.
Directed Acyclic Graph:
A DAG or a Directed Acyclic Graph is created with the help of the addresses and the transactions made on the Bitcoin network.
While analyzing DAG, you will need to break the whole graph into two smaller units.
You can create the first DAG with the Bitcoin addresses by tracing the flow of coins between the users and the addresses.
The second DAG can be constructed after the transactions are analyzed over time.
Here the second DAG is more important because it represents each of the transactions as a node and also directs edges between the target and Bitcoin source.
Therefore, this means that you will be able to determine whether or not the input of one particular transaction matches with the output of another.
You can then verify the other details of the transaction and then validate it so that it can be added to the chain.
Summarily, you can reject a transaction if the DAG reveals that it is performed repeatedly either by several entities or by any identifiable community or even conducted by a single entity.
Typically, when you break the whole Bitcoin system down into two DAGs it will help you to identify and group the behaviors of the transactions as well as the Bitcoin users over time.
You can use several data sources for this which includes:
- A directory of all Bitcoin users that contains all information off network that will allow you to monitor activity on the blockchain and
- A common routing behavior and transaction usage to map IP addresses with Bitcoin addresses to determine the geographical usage.
In addition to that, it will also help you to use temporal and flow analyses if you want to make a case study later on.
You will also need to analyze different graphs to know about the transaction behavior.
This will help you to take your algorithmic network analysis to the next level.
You will be able to know the gradual development in the behavior of Bitcoin transactions and the ways in which the Bitcoin addresses adjust over time.
Apart from that, when you use machine learning in advanced analytical techniques you will also be able to identify the pseudonymous nature of the Bitcoin addresses.
All these can be achieved when you analyze the graphs of the major Bitcoin transactions using a set of sub-graphs.
This will help you to identify and analyze the multiple distinguishing behaviors of the transaction flows.
When you see a binary structure of the long successive chains of transactions, you can even distinguish the fork merge patterns.
These patterns will further help you to identify the common practices of the users.
If you want to analyze these patterns on a significant scale you may use algorithmic techniques and automated software.
When you use automated software for analyzing a transaction while mining Bitcoin, you will be able to parse the blockchain for addresses and transactions.
You will be able to make much better forensic analysis with a framework and augment the results with several other data that you may have collected from the web.
This will help you to group, visualize, and contextualize the different graphs representing Bitcoin transactions.
Eventually, you will be able to identify the extorted addresses and associate the usernames.
And, if you want a better analysis of Bitcoin transactions, you can also use the automated analysis tools algorithmic modules.
This will help you to study, deduce and predict any particular patterns on the blockchain network.
If you want to make a deeper analysis of the transactional behavior on the blockchain you can use the algorithmic analyses as well.
This will allow you to know the fundamentals of Bitcoin such as its inherent data structure, activity of the users, and others.
This may also help you to identify transactions carried out by ransomware by distinguishing the unusual behaviors on the blockchain network.
There are also a few other proven risk management techniques that you can follow such as finding the address where a transaction is coming from or going to.
You can also apply some more advanced graph analysis techniques to the sub-graphs to find other important information such as the frequency of a Bitcoin address being reused, how changed addresses are splitting bigger transactions into smaller amounts, incoming edges and connectedness of the nodes on the network.
Also, you will be able to track miner behaviors, where the amount is split or accumulated, and other payment typologies.
All these will help you to create a user graph and outline the entire process of clustering step by step and get a useful clustering coefficient.
Other analyses of the user graph include time series view and distribution of wealth, nodes with the strongest connectivity and centrality analysis.
All these will enable you to know about the most active nodes on the blockchain network.
Typically, when you have a large set of analyzed data you will have a much better understanding of the user graph.
Machine Learning Techniques
You will be better off as a miner and validating transactions and ascertaining their authenticity when you implement some machine learning techniques to your analysis process.
In simple words, machine learning is a process in which you ‘teach’ the machines to perform any given task by them efficiently.
In this process the computer is trained to learn from the data input.
This process involves two specific algorithms namely supervised and unsupervised learning algorithms.
The supervised learning algorithms typically deal with training data that correct responses to the data fed in.
Therefore, this training data can categorize the future data objects.
The supervised machine learning techniques gathers and analyzes data to find several useful information regarding activities on the chain, scams, hosted wallets, exchange services, personal wallets and even mining pools.
For this, different types of data are used such as:
- Transaction data that includes hash, input and output address, value, and timestamp
- Address data that includes addresses as well as the value and number of transactions and
- Counterparty data that includes counterparty address, name, value, and category.
It also includes exposure that helps in risk calculation.
This is done on the basis of the cluster input and output number that come from or to any specific service category.
When you look at such detailed anatomy of a Bitcoin cluster created by supervised machine learning you can easily break down the structure of the cluster to group the controlling entities.
If you want to get some added advantage you can use more sophisticated procedures based on deep learning of the neural networks to find out the hidden representations on the network structure or a graph.
This will help you to detect traditional AML anomalies and find out the good and bad transactions easily which will help you in validating them while you mine Bitcoin and minimize the chances of false positives and false negatives occurring.
On the other hand, the unsupervised learning algorithms typically classify or interpret evocative outputs without having any prior knowledge of the data structure or domain.
The unsupervised algorithms are very powerful to learn the features of the set of data and identify any anomalies within it.
An example of an unsupervised machine learning method is clustering.
In this process, input data items that expose distinctly different and similar attributes are grouped together.
In Bitcoin mining such clustering helps in finding the transactions and the addresses that are controlled by a common individual.
The unsupervised machine learning techniques allows the algorithm to set up its own labels based on the data input and will also help in executing the machine components quicker.
It will also detect any abstract context or anomaly in the data passing through it and form a cluster label that can be used as a reference for the supervised SVM or Support Vector Machine algorithm.
It is then used to verify the cluster contents.
This eventually helps in validating the transactions easily and quickly based on the results obtained.
All these are generative machine learning models that use PCA or Principal Component Analysis and k-means clustering among some of the common methods that help in building a chain of good transaction blocks on the Bitcoin network.
You can also use reinforcement learning that is typically an approach that involves both supervised and unsupervised learning algorithms.
However, for that you will need to use deep learning models to have a deeper understanding of the data to be analyzed.
Deep learning in graph networks is an approach that helps in learning the role of a node in the network.
Usually, deep learning is a process that is based on the ‘struc2vec’ algorithm.
In this process, the nodes that are in close proximity are considered and analyzed on the basis of the embedded data.
This further helps in knowing the similarities between the network and nodes. This may not belong to components that are directly connected.
This entire process of learning node embeddings helps in knowing the relationships between the nodes in the target network.
Another useful deep learning method is Graph Matching Network or GMN.
In this process, the similarity in the graph score is calculated and analyzed by using Graph Neural Networks or GNN.
GNNs typically use the graph structured data encoded and help in learning the unlabeled graph structures on complete graphs.
This way you can understand the similarities between the nodes by comparing the input graphs with others.
Eventually, it will help you to determine whether or not there are any differences associated with the node and their edge features.
When the techniques are used on a Bitcoin graph, it allows training the machine and learning the parameters and behaviors the network may develop in the future.
Another good technique for deep learning is the Graph Convolutional Networks or GCN.
This technique uses neural networks that further uses embedding of the relational information between relationships and nodes in machine learning.
The GCN clusters the in and out degrees of the nodes and propagates them as features on the nodes of a particular network.
Human and machine relationship:
If you want to analyze a Bitcoin blockchain network as a graph you will need to combine machine learning with human subject matter.
This is very important in machine powered analytics because it will help in contextualizing the data.
The efficiency of analysis is further enhanced when high performance computing is applied to huge amounts of data.
The most common approach taken for this is to cluster data of the significant nodes in the Bitcoin network graph.
This will allow applying graph algorithms in connection to community detection and centrality.
When labels are added to the collected data and it is combined with the Bitcoin data and external data sources it will help you to have a much better structural knowledge which includes and is not limited to:
- In, out, or change addresses
- Amount sent
- Amount received
- Network depth
- Service labels and
- Frequency of address reuse.
In future, when deep learning will be combined with Artificial Intelligence or AI it will further augment the analytic process for any given Bitcoin network in the ecosystem.
Transaction Analysis Techniques
Finally, it comes to the Bitcoin transaction analysis techniques, which ideally is your prime objective as a Bitcoin miner.
One of the best techniques to follow is to use cluster analysis.
This should be done ideally on the entire Bitcoin blockchain network to identify with the core financial infrastructures.
Furthermore, such type of analysis will also help in identifying connections and speculations considering the total amount of transactions for every seed address.
This includes the number of coins sent and received.
When this process is used for analyzing individual transaction levels, it considers the input and output addresses, the timestamps, and the number of coins transferred.
All these will allow you to build a model that will specify the network depth and identify any service from the blockchain API or Application Programming Interface that will point to Bitcoin exchanges.
The best part of this process is that it will offer you a complete analysis of the continuum of the network model.
Moreover, you can also use the applied graph analysis technique on graphs to analyze the centrality and classify addresses.
This will allow you to know which nodes acted as the collectors along with the services that the addresses corresponded to.
If you conduct a time series or longitudinal analysis you will be able to see the profile of the addresses as well.
It will also allow you to take a look at the history of the specific collector address.
This is very useful for understanding the transactional behavior along with the incoming and outgoing relationships.
Overall, it will help you to identify the transaction pattern of the Bitcoin graphs.
It is also good to use the Cumulative Distribution Function or CDF which is comparatively a simple analytical method that will allow you to know the blockchain specifics.
This will be done typically on the basis of the varied input transactions and multiple change addresses.
Ideally, all of the techniques mentioned above for Bitcoin analysis may vary depending on the continuum, elements, and the data attributes.
Therefore, you can see that Bitcoin mining is not just using expensive mining hardware and expect to be successful in it.
You will need to do a lot of data analyses to validate every transaction accurately for that, as suggested in this article.