Airacle

Project Title: AI-Powered Oracle to Combat Airdrop Farming Problem Statement: Airdrop farming, where users exploit token distribution campaigns by creating multiple wallet addresses, is increasingly being automated by sophisticated bot services. This manipulative behavior not only distorts the intended purpose of airdrops but also undermines the integrity of the entire crypto industry. By skewing token distributions, these practices deprive genuine participants of their rewards, erode trust in blockchain projects, and discourage legitimate user engagement. As airdrop farming becomes more prevalent, it threatens the adoption and progress of blockchain technology, posing a significant barrier to the growth and sustainability of the crypto ecosystem.

TL;DR: Our project proposes an AI-powered oracle, built on Chainlink and deployed on the Arbitrum blockchain, designed to detect and prevent airdrop farming across blockchain networks. By leveraging advanced machine learning and graph analysis techniques using Blockscout for data collection, this solution ensures fair token distribution, promoting trust and integrity within the crypto community. This initiative is crucial for safeguarding the industry's growth and encouraging broader adoption of blockchain technology.

Detailed Description: The AI-powered oracle integrates with Chainlink, a leading decentralized oracle network, to provide real-time predictions and prevent airdrop farming. Here's how it works: Data Collection: Utilizing Blockscout, the oracle extracts historical transaction data and wallet interactions from the Arbitrum blockchain. Graph Construction and Analysis: The data is used to build a local graph using NetworkX, representing wallet interactions and applying community detection algorithms to identify clusters of related addresses. Feature Extraction and Machine Learning: Features are extracted from the graph, and machine learning models are trained to classify addresses based on their activity patterns. These models are designed to detect signs of manipulative behavior indicative of bot-driven airdrop farming. Chainlink Oracle Implementation: The AI model is integrated into a smart contract oracle using Chainlink. This allows airdrop contracts to query the oracle for real-time predictions, assessing the likelihood that an address is a bot or involved in airdrop farming. Real-time Predictions and Blocking: When an airdrop claim is made, the airdrop contract calls the Chainlink oracle, which provides a risk score based on the AI model's analysis. Suspicious addresses can then be blocked from claiming tokens, ensuring fair distribution.

Key Differentiators:

Focused Detection for Airdrop Farming: Unlike general bot detection, this model is specifically designed to detect airdrop farming patterns, providing a more targeted and effective solution. While some bots might be acceptable in certain contexts, airdrop farming by non-bots is also problematic. This granularity ensures better user experience and more accurate detections.

Protocol-Specific Customization: The approach can be adapted to different protocols and contexts. For instance, patterns indicating airdrop farming might differ from those for free NFT minting. This adaptability ensures that the detection model remains relevant and effective across various use cases.

Lower Latency Inferences: By focusing on specific patterns related to airdrop farming, the model can operate with lower latency, providing quicker responses and enhancing the overall efficiency of the airdrop process.

Benefits to the Crypto Community:

Fair Token Distribution: Ensures tokens reach genuine users who will contribute positively to the network, rather than exploitative actors. Enhances the fairness and credibility of airdrop campaigns, attracting more legitimate participants.

Improved Security and Trust: Reduces the risk of network manipulation and exploitation by blocking suspicious addresses. Builds trust among users and developers through transparent and equitable distribution practices.

Encourages Sustainable Growth: Helps projects achieve their marketing and user acquisition goals by targeting real users. Supports long-term engagement and retention by rewarding genuine participation and support.

Scalable and Adaptable: Continuously trainable AI models can adapt to new attack vectors and strategies used by airdrop farmers. Applicable to various blockchain-based rewards and incentive programs, making it a versatile tool for the ecosystem.

By addressing the pervasive issue of airdrop farming with a sophisticated AI-powered oracle, this project not only protects individual blockchain projects but also strengthens the overall integrity and sustainability of the broader crypto ecosystem.

Project Overview This project implements an AI-powered oracle built on Chainlink and deployed on the Arbitrum blockchain. It uses advanced machine learning and graph analysis techniques to detect airdrop farming in real-time, leveraging Blockscout for data collection.

Key Components Airdrop Smart Contract: A demo contract that integrates with the oracle to verify claim requests. Chainlink Oracle: Facilitates communication between the smart contract and our backend service. Backend Service: Performs real-time analysis of wallet activities to detect airdrop farming. How It Works When a user attempts to claim an airdrop, the smart contract queries our Chainlink oracle. The oracle calls our backend service, which initiates the analysis process. The backend service uses Blockscout to fetch data about the wallet's interactions on the Arbitrum blockchain. An AI model analyzes this data to determine the likelihood of the wallet being part of an airdrop farm. The oracle returns a probability score to the smart contract. The smart contract decides whether to allow or block the claim based on a predefined threshold.

AI-Powered Analysis Our solution employs a sophisticated AI model that:

Explores the activity around the wallet in real-time. Queries the wallets and contracts it has interacted with. Incrementally explores interactions of other addresses in the neighborhood. Builds a neighborhood graph using random sampling. Detects communities inside the constructed graph, isolating highly relevant addresses. Applies a similarity model to check how likely the community behaves together. Determines if the community is forming an airdrop farm based on behavioral patterns.

Technology Stack Blockchain: Arbitrum Oracle Service: Chainlink Data Source: Blockscout Graph Analysis: NetworkX Backend: Python Flask