# Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions
## 1 Introduction  
Blockchain technology has emerged as one of the most transformative innovations of the 21 st century, offering unprecedented capabilities in distributed storage, peer-to-peer transmission, strong confidentiality, and convenient traceability [1, 2, 3]. Since its inception with Bitcoin in 2008, blockchain technology has undergone substantial evolution, extending its applications far beyond cryptocurrencies into a wide array of sectors, including financial services, supply chain management, healthcare, emergency response, and the management of Internet of Things (IoT) ecosystems [4, 5, 6, 7, 8, 9, 10]. Despite the inherent security features of blockchain technology, such as cryptographic verification and distributed consensus, blockchain networks remain vulnerable to various attacks and fraudulent activities, necessitating robust anomaly detection systems [11, 12, 13, 14, 15]. Recent reports indicate a significant surge in fraudulent activities, attacks, and security incidents within blockchain networks, posing a serious threat to users' personal assets [16, 17, 18, 19, 20, 21]. Anomalies in blockchain networks can take various forms, including malicious accounts, Ponzi schemes, PoW vulnerabilities, cryptojacking, spam transactions, walletattacks, phishing scams, and more [22; 23; 24; 25]. Detecting anomalies in blockchain networks is particularly challenging due to factors such as the immutability of records, the large volume and dynamic nature of transactions, the sophistication of attacks, and the class imbalance caused by the rarity of anomalous nodes [26; 27; 28; 29; 30]. These challenges have made anomaly detection a key area of research in blockchain systems. This growing interest has led to the publication of numerous surveys and review papers that aim to synthesize the state of the art in this field [31; 32; 33; 34; 35; 36; 37; 38]. However, the majority of these surveys rely on traditional, narrative methodologies--they classify techniques, models, or domain-specific applications, often without providing a comprehensive, data-driven overview of the knowledge structure or research evolution. For example, the survey article by [PERSON] et al. [31] provides a detailed overview of the integration of anomaly detection models at various blockchain layers, emphasizing the importance of timely response to threats. Similarly, [PERSON] et al. [37], in their review, focus on the characteristics of anomalous transactions and methods for their detection in both financial and non-financial sectors. Other surveys, such as the studies by [PERSON] et al. [32] and [PERSON] et al. [33], analyze the use of deep learning and unsupervised algorithms in the context of blockchain anomalies. They highlight the potential of these methods for detecting complex and novel types of anomalies without the need for pre-labeled data. The review by [PERSON] and [PERSON] [34] examines the interaction between artificial intelligence and blockchain, specifically how AI techniques contribute to improving the accuracy and adaptability of anomaly detection systems. Similar conclusions are drawn in the survey by [PERSON] et al. [35], which demonstrates the effectiveness of combining blockchain and data mining methods for financial monitoring and fraud prevention. Additionally, the survey by [PERSON] et al. [37] offers a systematic analysis of real attacks on blockchain systems and describes modern protection mechanisms, including anomaly detection methods that combine cryptography and machine learning. Crucially, none of the existing studies incorporate a bibliometric approach, nor do they follow a systematic review protocol such as PRISMA to ensure replicability and transparency. The absence of bibliometric mapping means that current overviews do not adequately highlight influential contributions, emerging trends, or structural research gaps at a macro-level. This review fills a key gap by offering a structured, data-driven overview of blockchain anomaly detection research.