The evaluation of smart contract reputability is essential to foster trust in decentralized ecosystems. However, existing methods that rely solely on static code analysis or transactional data, offer limited insight into evolving trustworthiness. We propose a multimodal data fusion framework that integrates static code features with transactional data to enhance reputability prediction. Our framework initially focuses on static code analysis, utilizing GAN-augmented opcode embeddings to address class imbalance, achieving 97.67% accuracy and a recall of 0.942 in detecting illicit contracts, surpassing traditional oversampling methods. This forms the crux of a reputability-centric fusion strategy, where combining static and transactional data improves recall by 7.25% over single-source models, demonstrating robust performance across validation sets. By providing a holistic view of smart contract behaviour, our approach enhances the model's ability to assess reputability, identify fraudulent activities, and predict anomalous patterns. These capabilities contribute to more accurate reputability assessments, proactive risk mitigation, and enhanced blockchain security.

This raises the fundamental question of whether it is possible to systematically differentiate reputable projects from those that may not be. While existing research has primarily focused on detecting fraudulent activities--such as scams, Ponzi schemes, and network anomalies--these efforts remain centered on identifying and flagging illicit behavior rather than providing a holistic assessment of a project's overall reputability. Several studies have explored the detection of illicit activities on the Ethereum blockchain [8], the identification of Ponzi schemes [17], for anti-money laundering [15] and anomaly detection within the network [13]. While these contributions enhance our understanding of fraudulent behavior, they do not directly address the broader issue of evaluating whether a project itself is reputable. Given the growing number of Ethereum-based initiatives, the need for a systematic approach to assessing project reputability becomes increasingly evident. Distinguishing between legitimate and potentially deceptive ventures requires a dedicated methodology that extends beyond merely detecting illicit activity. By establishing such an approach, stakeholders, including investors, developers, and regulators can make more informed decisions, mitigate risks associated with unreliable projects, and foster a more secure and transparent investment landscape within the Ethereum ecosystem. This research aims to identify projects that are likely to be reputable by comparing them against a model comprised of data associated with a list of reputable projects from a source deemed to be trust-worthy. We therefore, define the following project aim to: develop a comprehensive methodology for identifying likely-reputable Ethereum Blockchain based projects using transactional data and machine learning techniques.