Cross-chain bridges are essential for enabling interoperability between diverse blockchain networks. At the same time, these technologies have introduced new avenues for illicit financial activity, such as chain-hopping, a method used to obscure transactional provenance by rapidly transferring assets across multiple chains. This study analyzes cross-chain token flows on the Wormhole bridge, focusing specifically on identifying suspicious token transfer patterns. We propose a methodology inspired by state-of-the-art anomaly detection techniques to identify and examine suspicious transaction patterns. Our findings provide valuable insights into cross-chain analysis and lay the groundwork for developing monitoring systems that can detect and analyze illicit behavior within cross-chain ecosystems.

Blockchain networks securely record transactions and enable decentralised transactions using cryptocurrencies. However, the pseudonymity nature of the participants makes the blockchain network a platform for illegal activities, such as money laundering, which poses significant threats to financial security and regulatory compliance. Money laundering activities undermine the integrity of financial systems, foster criminal enterprises, and enable tax evasion. This research explores the impact of timestamp-based (Time step) features in detecting money laundering activities within the Bitcoin network, utilising the Elliptic++ dataset. A correlation-based analysis revealed that the first block appeared in feature was the most strongly correlated with the Time step. Additionally, classification results highlighted XGBoost as the most effective classifier, with the first block appeared in feature identified as the most influential, based on Shapley values from the eXplainable Artificial Intelligence (XAI) technique.

Smart contracts are a key part of blockchain applications, and attackers can exploit them to manipulate contract behaviour or steal assets. Smart contracts often contain security vulnerabilities, either accidentally introduced by developers or due to flawed business logic. In this paper, we focus on finding an optimal Machine Learning based framework for detecting vulnerable smart contracts by analysing the smart contracts as embedding vectors. CodeBERT, a pre-trained transformer model, is used for feature extraction in the proposed framework. The framework has shown approximately 97% accuracy in detecting smart contracts that contain various vulnerabilities. Additionally, the research explores the performance of CodeBERT variants for this task. The results of the experiments have proven the favourability of this framework in detecting vulnerable smart contracts.

Blockchain technology has been integrated into a wide range of applications in various sectors, such as finance, supply chain, health, and governance. However, the participation of a few actors with malicious intentions challenges law enforcement authorities, regulators and other users. These challenges revolve around dealing with an array of illegal activities such as asset trades in dark markets, receiving payments for cyber-attacks, and facilitating money laundering. Developing an efficient mechanism to identify malicious actors in blockchain networks is a pressing need to build confidence among the stakeholders and ensure regulatory adherence. The raw data of blockchain transactions do not readily reveal the dynamic behavioural changes and their interconnection between transactions and accounts. These behavioural patterns can be useful for identifying malicious actors. Machine Learning (ML)-based models for early warning and/or detection are considered one of the potential approaches. In ML, feature engineering plays a crucial role in enhancing the predictive performance of a model. This study proposes different categories of features and unified feature extraction approaches for raw Bitcoin and Ethereum transaction data and their interconnection information. As far as we are aware, there has been no study that considered a feature engineering approach for identifying malicious activities. The significance of the engineered features was validated against eight classifiers, including Random Forest (RF), XG-boost (XG), Silas, and neural network-based classifiers. The results showed that these features contribute to higher classification accuracy and higher Area Under the Receiver Operating Characteristic Curve (AUC) value for both Bitcoin and Ethereum transactions. This work also analysed the influence of engineered features in classification using the eXplainable Artificial Intelligence (XAI) technique SHapley Additive exPlanations (SHAP) values. The feature importance scores confirmed the significance of the proposed engineered features towards implementing classification models to identify, target and disrupt malicious activities in blockchain networks.

Blockchain is a digital technology built on three pillars: decentralization, transparency and immutability. Bitcoin and Ethereum are two prevalent Blockchain platforms, where the participants are globally connected in a peer-to-peer manner and anonymously perform trade electronically. The vast number of decentralized transactions and the pseudo-anonymity of participants open the door for scams, cyber frauds, hacks, money laundering and fraudulent transactions. It is challenging to detect such fraudulent activities using traditional auditing techniques, since they need more processing power, time and memory for complex queries to join combinations of tables. This paper proposes several algorithms to extract the transaction- related features from the Bitcoin and Ethereum networks and to represent the features as graphs. Moreover, the paper discusses how visualisation of graphs can reflect the anomalies and patterns of fraudulent activities.