Detection and Analysis of Ethereum Energy Smart Contracts

As blockchain technology advances, so has the deployment of smart contracts on platforms, making it exceedingly challenging for users to explicitly identify application services. Unlike traditional contracts, are not written in a natural language, difficult determine their provenance. Automatic classification offers keyword-based contract queries and streamlined effective management contracts. In addition, advancement is accompanied by security challenges, which generally caused domain-specific breaches implementation. The development secure reliable can be extremely due vulnerabilities constraints associated with various business logics. Accordingly, based domain transaction context greater insight into syntactic semantic properties that class. However, despite initial attempts at classifying Ethereum there been no research identification deployed transactive energy systems exchange purposes. this article, response widely recognized prospects blockchain-enabled towards an economical transparent sector, we propose methodology detection analysis First, parsed transforming code elements vectors encapsulate characteristics each term. This generates corpus annotated text as balanced, representative collection terms use builder embedding layer annotate conjunction machine learning models results accuracy 98.34%. Subsequently, source scheme applied identified uncover patterns segment distribution, predominant adoption certain functions, recurring across network.