Abstract:
"Decentralized Autonomous Organizations (DAOs) are pivotal in the blockchain ecosystem, fundamentally altering how decisions are made and executed within these decentralized entities. However, the integrity and security of DAOs can be compromised by malicious proposals that exploit vulnerabilities within the system.
This research addresses a significant gap by introducing a novel risk analysis architecture aimed at identifying threats posed by abnormal proposals in DAOs. The architecture employs parametric analysis and address screening. Parametric analysis utilizes statistical outlier detection techniques to analyze proposal actions and detect abnormalities in function parameters based on historical data. Address screening assesses the integrity of associated addresses by cross-referencing them against known scam and sanctioned addresses, among other criteria. This dual approach enhances the detection of potential threats and improves the informed decision-making capabilities of DAO participants. By focusing on the specificities of smart contract interactions within DAOs, this solution extends beyond existing network-level security measures, contributing significantly to blockchain security and software engineering.
To test the novel statistical component of the solution architecture, synthetic data generation was followed by evaluation metrics calculation, yielding an precision of 92.3%, accuracy 85%, f1-score 80% and recall of 70.5%. Based on the outlined benchmarking results, it is clear that the proposed novel algorithm performs comparatively better than the considered mainstream statistical algorithms. This research ultimately aims to fortify the reliability and trustworthiness of DAO operations, ensuring their role as secure platforms for collective decision-making."
