Empirical Vulnerability Analysis of Automated Smart Contracts Security Testing on Agricultural Blockchains

Abstract

Abstract This study endeavors to uncover and rectify potential vulnerabilities within smart contracts, specifically within the framework of an agricultural block-chain setting. The overarching goal is to bolster the security and credibility of agricultural smart contracts, thereby establishing a foundation of trust and dependability in block-chain-driven agricultural ecosystems. The study’s primary focus lies in the synthesis of two innovative techniques: the ABC-GOA optimization and ensemble learning model. ABC-GOA optimization methodically windows down optimal feature selections from the set of features, which are extracted by AST from smart contract dataset, ensuring that only the most pertinent attributes are considered. This step augments the precision and efficacy of subsequent processes. Complementing this, the ensemble learning model ingeniously amalgamates diverse individual models, culminating in a collective classification approach that is more robust, insightful, and proficient. The results offer a resilient shield against potential threats and vulnerabilities inherent to smart contracts in agriculture, thereby fortifying the reliability and safety of block-chain-powered agricultural systems. This research contributes significantly to advancing secure and efficient smart contract deployment in the dynamic realm of agricultural block-chain applications.