# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 3 Research Methodology  
As stated in the Introduction, the main objective of this paper was to review the interplay of machine learning methods and blockchain data for anomalies and fraud detection. Therefore, this research was conducted by reviewing the present state of knowledge of applications using blockchain technology and machine learning methods. While designing this research paper, we formed three research questions, which are addressed and answered in the paper. The research questions are as follows:  
**Research Question 1 (RQ1). **: _What are the core elements for defining the taxonomy of machine learning methods used for anomaly and fraud detection in blockchain systems?_  
**Research Question 2 (RQ2). **: _Which machine learning methods are used for the intelligent data analysis of anomalies using data stored in a blockchain?_  
**Research Question 3 (RQ3). **: _What type of applications emerge from the use of machine learning methods within a blockchain environment?_  
Based on the research questions, the review was undertaken to address the following specific goals:  
* To review machine learning methods used for the intelligent data analysis of anomalies using data saved in a blockchain environment. * To synthesise a taxonomy of ML methods used for specific purposes. * To review applications that benefit from blockchain technology and machine learning algorithms. We started our study with an extensive literature search in several scientific abstract databases. In order to collect the required articles, the following search string was used:  
(\"blockchain' OR ''block chain'' OR ''distributed ledger'' OR ''smart contract'' OR ''cryptocurrency'') AND (''data mining'' OR ''classification'' OR ''machine learning'' OR ''AI'' OR ''preprocessing'' OR ''deep learning'' OR ''neural network'' OR ''artificial intelligence'' OR ''anomaly detection'')The search string was modified to meet the requirements and limitations of each selected search engine. Initially, the search was conducted so that the engines took the entire text of the articles into account. This led to a large number of results, and a large number of retrieved articles that were not relevant to our study. In order to fix this, the search was limited to include only abstracts and keywords. Additionally, the search was only limited to include results published within the last five years (2017 to 2021). The search was conducted between the 11 th and 14 th of June, 2021. The following search engines and scientific databases were used: ACM Digital Library, IEEE Xplore, Science Direct, Springer Link, and Web of Science. Table 1 shows the number of results obtained from each database. After the results had been collected, they were also checked to exclude duplicates, both within the databases and given by different databases. Table 2 shows the number of duplicates found among the databases. Given that Web of Science indexes articles that are hosted by other databases, it can be seen that most of the duplicates were found when comparing a specific database to Web of Science. Altogether, 18 duplicates were found within the databases, and 522 across each database pair. A total of 540 duplicates were excluded. We also defined several additional exclusion criteria that resulted in the removal of papers from our selection pool:  
* Research not written in the English language,
* The full text of the article was not available, and
* The method, evaluation process, and results were not described. After inspecting the title, keywords and the abstracts, 206 papers were initially selected for our research. However, one paper was not written in English, and we could not obtain the full text for nine papers. Therefore, these 10 papers were also excluded. A full text inspection was conducted on a total of 196 studies, and 130 were selected as relevant for our research. As we can observe in Figure 2, there has been an increasing trend in using machine learning techniques in blockchain anomaly detection.