# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 1 Introduction  
Blockchain technology [1] might be considered one of the most disruptive technologies of the last decade, which can revolutionise business processes within the private and public sectors. It offers the means to secure processed transactions in distributed and decentralised environments, providing transparency and immutability [2, 3]. Nevertheless, there are still several challenges associated with technology application related to security, scalability, interoperability, regulation. On the other hand, machine learning (ML) applications have emerged in recent years, due to the availability of vast amounts of data and the capacity of ML algorithms to provide systems with the ability to learn and improve automatically using past data [4, 5]. Blockchain technology can benefit from the use of ML algorithms while taking advantage of their ability to provide an analysis for an enormous amount of data. It can enhance the security of such systems significantly [6, 7, 8, 9].  
The applications that benefit from blockchain technology and machine learning algorithms rise promptly within different domains, such as healthcare, fintech, and the energy sectors and for different purposes, such as anomaly, fraud and malicious activity detection, biometrics' monitoring and disease detection, etc. Several reviews have dealt with these topics in recent years. Some of them address the integration of blockchain technology and artificial intelligence among other technologies to achieve decentralised authentication [6], to enable different features within the 5G networks [8], or to achieve the de-anonymisation of bitcoin addresses or entity recognition within cryptocurrency transaction networks [10]. Other works provided have surveyed the use of one or both technologies separately within a specific domain, such as Healthcare [3, 11, 12, 13, 14], Agriculture [15], Construction Engineering and the built environment [16, 17], or across several domains such as Data Management and IoT [3, 18], Blockchain industrial applications [2, 19], or addressing security and privacy issues [7, 9, 20]. A detailed insight into these reviews (presented in Section 3) reveals thatnone of these provide a comprehensive review of all applications regardless of the domain where the blockchain technology and machine learning techniques are used to complement each other, offering complete solutions for anomaly and fraud detection. Therefore, we contribute to the body of knowledge by (1) Providing a comprehensive review of applications where the synergy of blockchain technology and machine learning algorithms is used to detect anomalies, (2) Discovering all main machine learning methods used and the types of data they exploit, (3) Offering a taxonomy of machine learning methods used to enhance blockchain technology, serving a specific purpose.  
The structure of the paper is as follows. Section 2 provides fundamental information on blockchain technology. The research methodology used in the review, research questions and taxonomy, as well as an overview of existing reviews covering those topics are presented in Section 3. Sections 4 and 5 provide a detailed analysis of the machine learning methods used for intelligent data analysis and review of applications, respectively. A discussion can be found in Section 6, while Section 7 summarize the directions of the further development of the research field and issues to be addressed. Finally, conclusions and future trends are presented in Section 8.