A new approach to DeFi security

DeFi (decentralized finance) is an ecosystem of financial services built on blockchain technology (lending, exchanges, staking), where there are no banks or centralized intermediaries, and users interact directly through smart contracts.

Researchers have introduced the TAS-GNN model, which uses graph neural networks to combat Sybil attacks, whitewashing, and camouflaged fraud on DeFi platforms, where malicious actors cultivate artificial reputations before executing exit scams

❗️The problem with current solutions Typically, trust systems in DeFi focus solely on positive connections, such as interactions and fund transfers between participants. However, an attacker can artificially create such a network and gradually boost their reputation among their own accounts.

The TAS-GNN model takes into account two types of signals: 🟠trust (normal, verified interactions between participants) 🟠distrust (suspicious or anomalous connections, such as groups of accounts that only interact with each other)

Instead of relying on a single view of connections, the system builds a deeper analysis of network behavior, revealing not only typical interactions but also isolated or artificially created clusters.

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