# Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey
## 2 Related Work  
So far, several survey papers have been published to encircle the applications of blockchain technologies in various research areas such as cloud computing [4; 44], database systems [45], digital twins [46; 47], educational technologies [48], e-voting development [49], interoperability [50], smart contracts [51; 52; 53], internet of things (IoT) [54; 55; 56; 57; 58; 59; 60], and more. In addition, a large number of surveys address issues related to system security, cybersecurity, and privacy of blockchain frameworks [61; 62; 63; 64; 65; 66; 67; 68; 69]. Although the above-referred works address to some extent issues related to anomaly detection and deanonymization, in this section, we turn our attention to the following studies that appear to be more related to the current endeavor. [PERSON] et al. [30] investigated the application-based domains of anomaly detection, providing a categorization of the relative methods and types in terms of learning modes and techniques. The application domains involved intrusion and fraud detection systems, industrial damaging, image processing, and medical studies. After identifying three general types of threats, the techniques were categorized in terms of supervised, semi-supervised, and unsupervised mechanisms that spanned over a wide range of algorithmic strategies such as neural and Bayesian networks, support vector machines, rule-based systems, nearest neighbor schemes, clustering-based approaches, and statistical tools. [PERSON] et al. [31] discussed a fundamental framework for each investigated anomaly detection category, encompassing information-theoretic and spectral techniques while incorporating existing grouping methods. As such, they refined a succinct categorization of the selected methodologies by meticulously outlining their pros and cons based on unique assumptions and criteria for defining the anomalies in each category, while reporting computational complexity analysis for each one of the studied methodologies. [PERSON] et al. [32] analyzed the usage of graph-based anomaly detection methods in fraud detection by creating a hierarchical classification framework to group the methods into certain categories depending on several criteria such as types of networks and anomalies employed. Moreover, they offered a list of major difficulties faced by graph-based structures in fraud detection, underling the difficulties that exist in that domain. [PERSON] et al. [27] postulated that ensembles of classifiers can effectively cope with certain vulnerabilities of blockchain frameworks such as security, abuse and cyber-attacks, criminal activity, money laundering, and so on. To carry out a systematic and solid presentation, they also focused on the strengths and weaknesses of the above-mentioned models, spotlighting their importance during various stages of the data analysis such as the data preparation and preprocessing stages. Even though blockchain has been acknowledged as the spearhead in the development of decentralized applications, it has shown certain failures regarding security issues in cryptocurrencies and smart contracts. An effective way to eliminate the impact of the above issues is the employment of data-mining models with specific metrics, criteria, and requirements that appear to have critical importance as far as the model's robustness is concerned. In this direction, some survey papers further delve into exclusively investigating the use of data mining and machine learning theory in detecting anomalies in blockchain networks. For example, from a detailed point of view, [PERSON] et al. [25] identified two major groups of approaches. The first group encompassed methods that have a general purpose without focusing on specific anomalies, and the second one included methods developed for specific types of anomalies. Based on well-defined criteria, each one of the above groups was further divided into several subgroups, where the description of their structure was also presented while analyzing the pros and cons of each subgroup.