# Enhancing Data Integrity in Blockchain Oracles Through Multi-Label Analysis
## 1 Introduction  
The oracle problem in blockchain represents a fundamental challenge for the effective functioning of smart contracts. These self-executing programs, defined by coded terms, depend on reliable external data to function correctly [1]. However, the integrity of these data is crucial, as any manipulation or error can lead to incorrect decisions, jeopardizing not only contract execution but also trust in the entire blockchain system. If smart contracts operate with inaccurate data, the consequences can range from significant financial losses to erroneous transactions. In this context, oracles serve as the bridge between blockchain and the external world [2]. Oracles can be classified into different categories based on their data sources and validation mechanisms. Software oracles extract data from digital sources such as APIs, for instance, obtaining cryptocurrency prices from platforms like CoinGecko or CoinMarketCap to facilitate financial contract calculations [3]. Hardware oracles gather physical data through IoT devices, such as temperature sensors in agricultural fields to trigger insurance mechanisms under extreme weather conditions. Consensus oracles, like Chainlink, aggregate data from multiple providers and validate it through distributed voting mechanisms among nodes [4]. Despite these advances, ensuring the integrity of oracle-fed data remains a critical challenge, as these systems are still susceptible to manipulation, errors, and latency issues. Existing solutions, such as decentralized oracles and consensus-based validation, have attempted to mitigate these risks, yet they remain insufficient. Decentralized oracles rely on the trustworthiness of their data sources, which can be compromised, while consensus mechanisms, though improving data validation, introduce delays and increase computational costs, making them less suitable for real-time applications [5]. These limitations indicate that, although current methodologies enhance data reliability to some extent, they do not fully resolve the oracle problem, leaving smart contracts vulnerable to incorrect or manipulated information [6]. To further improve data integrity, some approaches have explored using smart contracts to verify oracle data through predefined validation criteria. Additionally, machine learning techniques have been introduced to assess data quality dynamically, particularly in environments where high variability can compromise information integrity [7]. However, prior research lacks a structured approach to integrating machine learning with multi-source validation in a way that enhances both accuracy and computational efficiency. This study proposes the Integrity Multi-level Weighted Voting (IMWV) model. It aims to address these gaps by implementing a hybrid validation mechanism that assigns differentiated weights to various oracle sources based on their historical reliability and consistency [8]. Unlike traditional approaches that rely on static validation rules or decentralized consensus alone, the IMWV model dynamically evaluates the integrity of incoming data using machine-learning-based classification, improving the resilience of smart contracts against erroneous or manipulated information [9]. A key motivation for this research is the increasing frequency of real-world failures caused by unreliable oracle data. High-profile incidents in decentralized finance (DeFi), such as price manipulation attacks on lending protocols and erroneous contract executions due to faulty data inputs, underscore the urgency of enhancing oracle reliability [10]. For example, flash loan attacks exploiting manipulated price feeds have led to multi-million-dollar losses, revealing the vulnerabilities in current oracle validation mechanisms. The proposed model seeks to provide a more robust and adaptive solution for securing smart contract operations [11] by addressing these challenges.