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How can Machine Learning be used with Blockchain?

Machine Learning technology is one of the most trending technologies with amazing capabilities, whereas Blockchain is the heart of all cryptocurrencies. Blockchain technology is becoming popular day-by-day, as this allows any user to directly deal with others through a highly secure decentralized system without requiring any intermediatory. Machine Learning can be applied with Blockchain technology to make it more efficient and better. We will see how machine learning and Blockchain can be combined to get maximum results in this topic. Before starting, let's first understand the basics of both technologies.

How can Machine Learning be used with Blockchain

What is Blockchain?

Blockchain can be defined as a shared, immutable digital ledger that allows storing transactions and tracking assets within a highly secure network. Here the assets can be tangible (house, car, cash, land) or intangible (patents, copyright, brandings, intellectual property). As blockchain is immutable, which means once entered, data is irreversible.

Simply, we can understand blockchain as a type of distributed database system that stores any type of data, which is very difficult to hack, change or cheat the system. The main difference between a conventional database and a blockchain is that database stores data into tables, whereas a blockchain stores data into blocks that are chained together.

Blockchain is a decentralized system, which means it is not maintained by a centralized entity (individual, organization, or any group); rather, it is maintained by a distributed network.

A blockchain can store different types of information, but mainly this technology is used behind cryptocurrencies such as Bitcoin.

Components of Blockchain

  • Blocks: Each blockchain is made up of several blocks, where each block has three elements:
    • Data
    • Nonce
    • Hash
  • Miners: Miners are used to create new blocks through mining.
    How can Machine Learning be used with Blockchain

Nodes: A node can be understood as a device that contains a copy of the blockchain. For a complete transaction, there are different nodes, and each node owns a copy of the blockchain.

How does Blockchain Work?

How can Machine Learning be used with Blockchain
  • Whenever a transaction occurs, it is stored as a block in the chain.
    Whenever a new transaction occurs, it is saved as a block. The data block can store information as per your choice, such as Who, What, When, Where, how much, and any condition, such as the temperature of a food shipment.
  • Each block is connected to the ones before and after it.
    Each block is connected and forms a chain, and changes its positions as a change of ownership. Each block confirms the exact time of the transaction and is connected in such a secure way that no block can be altered or inserted between the two existing blocks.
  • Transactions are blocked together in an irreversible chain.
    The security of the complete blockchain is strengthened by each newly added block that verifies its previous block. In such a way, blockchain becomes immutable, and hence each transaction is irreversible.

How did Machine Learning come into Play with Blockchain?

Machine learning can be understood as a technology that learns from past data and improves performance with new data. Hence, we can say it is self-adaptive technology, and we don't need to add new rules manually. We can understand it with one of the popular examples of machine learning, "Spam Detection". It is software that automatically improves its performance of detecting spam and junk emails over time. It does this with the help of an underlying algorithm that helps it learn from data and make predictions on data.

When such capabilities of machine learning are combined with blockchain, it generates great opportunities and benefits for its users.

By using ML to govern the blockchain, the security of the chain can be enhanced to a great extent. Moreover, as Machine learning work better with lots of data, it can generate a great opportunity to build better models by taking advantage of the decentralised nature of blockchains.

The combination of both technologies can be a game-changer for the finance and insurance industries to identify fraud transactions.

Machine Learning in Blockchain-Based Application

1. Enhanced Customer Service

As customer satisfaction is one of the major challenges for each organization, companies are using different ML techniques to enhance their customer services. By combining Machine Learning with a blockchain-based application, customer services can be enhanced to a great extent.

2. Surveillance System

Security is an important concern of the people because of the increasing crime rate in the present scenario. Machine learning and Blockchain technology can be used for surveillance, where blockchain can be used for managing continuous data, and ML can be used for analyzing the data.

3. Smart Cities

Nowadays, Smart cities are evolving day by day and helping people to enhance their living standards by making their life easy. A smart city also involves machine learning and blockchain technologies that play a crucial role. For example, a smart home enabled with blockchain and Machine learning algorithms can be monitored easily and can provide device personalization to each individual.

4. Trading (Reinforcement Learning)

As blockchain is the key technology among most of the popular cryptocurrencies such as Bitcoin and Ethereum. These trading cryptocurrencies are becoming popular amongst retail investors and large financial institutions. Nowadays, traditional trading bots are embedded with powerful Machine Learning algorithms.

Reinforcement learning is a type of Machine learning commonly used with complex games and simulation programs. Reinforcement Learning is a viable approach to develop cryptocurrency trading strategies that are profitable and adaptive.

5. Optimizing Mining Strategies (Reinforcement Learning)

In the blockchain, the mining process plays a vital role. This process involves guessing a set of values to solve a function on a blockchain through different computer resources. The miner who solves the function can update the blockchain with valid pending transactions.

Taotao Wang, Soung Chang Liew, and Shengli Zhang authored a research paper, where they presented how reinforcement learning can be used for optimizing blockchain mining strategy for cryptocurrencies such as Bitcoin. In this paper, the author shows a way to use a multidimensional RL algorithm that uses a Q-learning technique for optimising cryptocurrency mining.

6. Tackling Cryptojacking (Deep Learning):

Another application of machine learning within the blockchain is for making it more secure. As different computational resources are used to mine cryptocurrencies, these can be targeted by the Cryptojackers who hijack these computational resources. Nowadays, these attacks have become common and hence need higher security. Different researchers have found a new method of detecting the presence of malicious programs that may hijack computer resources. One of such methods is SiCaGCN.

SiCaGCN is the system created by the researchers that identify the similarities between a pair of code. It consists of components of neural networks and different techniques of deep learning and the ML domain.

Benefits of Combining Blockchain and Machine Learning together

Combining Machine Learning and Blockchain together can generate enormous benefits for various industries. Below are some popular benefits of combining Blockchain and Machine Learning for the Organization:

  • Enhancing Security
    Data in Blockchain is much more secured because of implicit encryption of the system. It is the perfect system to store highly sensitive personal data, such as personalized recommendations.
    Although at its base, blockchain is secured, some applications or additional layers that are using blockchain can be Vulnerable. For such a case, we can take advantage of Machine learning. ML can help to predict the possible breaches or security threats in blockchain apps.
  • Managing the data Market
    Different big companies such as Google, Facebook, LinkedIn, etc., have a huge amount of data or large data pools, and this data can be very useful for the AI processes. However, such data is not available to others.
    But, by using Blockchain, various start-ups and small companies can access the same data pool and same AI process.
  • Optimizing Energy Consumption
    Data Mining is a high-energy consuming process, and it is one of the major struggles for different industries. However, Google has majorly solved this issue with the help of Machine Learning. Google does this by training the DeepMind AI so that it can reduce the energy consumption used for cooling the data centres by approx. 40 %.
  • Implementing Trustable Real-time Payment Process
    By combing Blockchain and ML, the most trustworthy real-time payment process can be implemented in the Blockchain environment.


With the above description, we can conclude that both Machine Learning and Blockchain perfectly complement each other. Both these technologies can be used as the pillars of future innovation.

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