Abstract
The Web 3.0 digital economy is built on blockchain infrastructure, facilitating socio-economic activities through digital assets like cryptocurrencies, NFTs, digital collectibles, and decentralized applications (DApps) such as decentralized finance (DeFi) and gaming finance (GameFi). Smart contracts serve as the backbone of DApps on public and permissioned blockchains (e.g., Ethereum, Solana, EOSIO, Findora, Antchain, ChainMaker). While their open deployment and transparency foster innovation, they also introduce significant financial risks.
This review analyzes Web 3.0 economic risks with a focus on smart contracts, categorizing risk perception technologies into three dimensions: smart contract encoding, functionality, and application. Key discussions include:
- Smart contract vulnerability detection: Challenges, common vulnerability types, and four methodological approaches.
- Smart contract scams: Prevalence, classifications, and data-driven recognition techniques.
- Illicit transaction detection: Behavioral patterns identified through blockchain transaction analysis.
We conclude by addressing research gaps and proposing future directions for risk mitigation in Web 3.0 ecosystems.
Core Keywords
- Web 3.0 digital economy
- Smart contract risks
- Blockchain vulnerabilities
- Decentralized finance (DeFi)
- Scam detection
- Illicit transaction analysis
- Risk perception technology
FAQs
1. What are the primary risks associated with Web 3.0 digital economies?
Web 3.0 risks stem from smart contract vulnerabilities, fraudulent schemes (e.g., rug pulls), and illicit activities like money laundering. The decentralized and immutable nature of blockchain complicates dispute resolution and regulatory oversight.
2. How do smart contract vulnerabilities impact DeFi platforms?
Vulnerabilities such as reentrancy attacks or logic flaws can lead to fund theft, protocol collapses, or manipulated tokenomics, undermining user trust and ecosystem stability.
3. What methods are used to detect smart contract scams?
Techniques include machine learning models trained on historical scam data, pattern recognition for Ponzi schemes, and anomaly detection in transaction graphs.
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4. Can blockchain transactions be traced for illicit activities?
Yes. Tools like clustering algorithms and network analysis identify suspicious transaction flows (e.g., mixing services or darknet market ties), though privacy coins pose additional challenges.
Future Research Directions
- Cross-chain risk analysis: Addressing vulnerabilities in interoperable protocols.
- AI-driven real-time monitoring: Enhancing scam detection speed and accuracy.
- Regulatory-tech integration: Balancing decentralization with compliance frameworks.