# Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning
## 1 Introduction  
The Blockchain is a distributed database, also known as a digital ledger. Blockchains are designed to be decentralized, meaning there is no centralized authority (e.g., a government, bank, or corporation) that can control it. Due to various factors, such as high compatibility with financial systems and the ability to support smart contracts [1], blockchain technology in banking and financial services is expected to expand rapidly around the world. The banking and financial services industry has recognized the value of blockchain technology, which helps customers in safe transactions. All the data on a blockchain are recorded accurately and can serve as a history that is available for all applications [2]. A blockchain is a constantly developing computerized record in pieces known as blocks which are connected and verified utilizing cryptographic hash functions. Data blocks are stored in a direct chain and each block in the chain contains information (for example, transactions in bitcoins), is time-stamped and cryptographically hashed. Blocks in the blockchain comprise block headers, block identifiers and Merkle trees (referring to the assembly of transactions). Blockchain is a constantly evolving technology. Due to its innovative characteristics, fresh applications are usually created incessantly over its framework. Blockchains are, in fact, an immutable collection of records that are cryptographically linked for auditing purposes [3]. It is similar to an accounting ledger system, but there is no single entity responsible for verifying the records. As illustrated in Figure 1, a blockchain is established by connecting valid blocks: the previous block's hash is contained in the current block. As a result, blockchains are identifiable and impervious to modification [4, 5]. Older blocks cannot be altered, as their hash would change if they were altered in anyway. The main focus on linking hashes in subsequent blocks is necessary to re-validate the network of the blockchain. Each person inside the network has a copy of the blockchain; as a result, any alterations can be cross-verified by other users [6]. These replicas of blockchain are revised whenever a new block is added. The new block is then visible to everyone, based on the administrator-assigned permissions [7; 8]. As transactions are written out on the blockchain, the entire transaction history is shared with every peer in the network. With blockchain, participants can cooperate legitimately and can make transactions over the web without the need for a trusted third party. Blockchain is extensively used in the Internet of Things [9; 10] and it also is at the basis of trust mechanism that can detect internal attacks [11]. Many researchers have proposed methods to improve security using machine learning algorithms [3; 12; 13; 14; 15; 16]. However, said models did not address the identification of risky and non-risky blocks in the blockchain that may effect the network after any transactions. To address the transaction classification problem, this study has concentrated on ensemble learning coupled with feature selection. This paper's contributions are outlined below:  
* An extensive literature review of Blockchain blocks analysis approaches with or without machine learning;
* Feature extraction during preprocessing;
* Improved feature selection;
* Application of ensemble base classifier in a stack-based and boosting approach. The performance of the proposed approach performance is compared to current work by different researchers using assessment measures, such as accuracy, precision, recall, and F-measure. The remaining portion of the paper is structured as follows: Section 2 refers to previously published writing that is relevant to our methodology. After a brief recapitulation (Section 3) of the essential intuitions about the techniques used, Section 4 describes the approach used in this paper, comprising features and the suggested feature selection strategy. The following Section 5 explains the details of the experiments and their outcomes.