TACKLING INSTANT LIQUIDITY DRAINING ATTACKS IN DEFI SMART CONTRACTS WITH HYBRID BLOCKCHAIN-AI SOLUTIONS

Abstract

Decentralized finance (DeFi) protocols are becoming increasingly targeted by cyber threats, such as liquidity drain attacks, smart contracts flaws that leverage instant loans, and increasingly sophisticated threats that include DarkGate ransomware. We develop a hybrid framework that integrates CTI and predictive analytics to facilitate improving consensus mechanisms in a blockchain network. The proposed framework is centered on three layers , a data collection and processing layer, a security oracle layer that engages to mitigate intervention, and a dynamic adaptive mechanism to reach consensus. A 250-node testbed was built and deployed with the Hyperledger Besu and Geth deployments of Ethereum incorporating hybrid GRU-BiLSTM which utilize GNN's for predicting attacks. The results reveal improvements of transaction processing TPS of up to +236%, settlement latency improved -75%, fork rate improved to less than 3%, and downtime improved from 15% to 1.5%. Statistical tests T-Test and ANOVA also reveal these were of high statistically significance at p < 0.01. This study emphasizes that bridging functional aspects of AI with adaptive consensus mechanisms will be an effective approach at combating advanced cyber-attacks while maintaining reliability and resilience in DeFi systems.