# Detection and Analysis of Ethereum Energy Smart Contracts
## 1 Introduction
However, despite the prevalent adoption of smart contracts in energy applications that streamline consumer and prosumer interactions towards a robust settlement process, no research has been conducted on the identification and analysis of energy smart contracts. Considering the multitude of deployed contracts, the lack of methodologies (such as classification models for the analysis of energy smart contracts) makes it challenging to gain insights into this ecological environment and to identify vulnerable smart contracts once deployed. To the best of our knowledge, there has been no contribution explicitly addressing the detection and analysis of energy smart contracts. The key contributions of this article can be summarized as follows. This paper demonstrates the significance of domain-specific classification and analysis tools for smart contracts. This is accomplished using a method that leverages contextual terms for classification of smart contracts using code and comments. The proposed classification pipeline provides a method for the detection and labeling of energy smart contracts using their source code. It uses feature engineering methods to produce domain-specific corpora, which are subsequently embedded within machine learning classification models. A domain-specific analysis is a method of deriving technical and language-dependent features to enhance the structural and procedural understanding of smart contracts. This is achieved by employing contextual terms at a lexical level, searching for key terms and attribute tags to develop the energy corpus that will aid in deciphering the code's context and retrieving energy contracts. By discerning the domains, we intend to reach higher levels of abstraction and handle the intricacies of DApps design specifications. The developed model captures the entire lexicon of transactions used for developing an energy smart contract. Finally, energy smart contracts are analyzed to identify patterns in the distribution of code segments, the predominance of specific functions, and recurring contracts across the Ethereum network. The proposed approach can be used by contract developers to track similar contracts deployed on Ethereum. Contract level representation is used to include the highest level of granularity in the classification; thus, the proposed method is applicable to any application domain by using the key terms and attributes pertaining to that domain. It develops the corresponding corpus using contextual terms, and the collected attributes are then used to train the classifier. This methodology can be further extended to include measures for anomaly detection and malicious contract detection in the context of energy smart contract analysis. Security practitioners can use it to investigate the potential vulnerabilities of energy smart contracts. The search for vulnerabilities can be optimized by factoring intriguing elements such as the semantics of the defects while vulnerable contract's syntactic representation is protected to the greatest extent possible. This an important step towards the design and development of auditing systems to address the identified vulnerabilities. The article is organized as follows. Section 2 introduces energy smart contracts and provides background information on smart contracts and natural language processing. In Section 3, related works are discussed to further highlight the contributions of this research. Section 4 describes the research methodology and the proposed classification pipeline, followed by Section 5 which showcases the classification results obtained by the baseline models. Finally, Section 6 summarizes the conclusions and future research directions.