(untitled) (Blockchain, Web3 & Digital Trust Journal p-ISSN 3117-597X and e-ISSN 3117-5988, 2025)
Blockchain networks are under sustained attack. In 2024 alone, exploits against smart contracts, consensus mechanisms, bridge protocols, and validator infrastructure resulted in losses exceeding USD 1.7 billion across DeFi, cross-chain bridges, and centralised exchange hot wallets. The attack surface is broad and growing: reentrancy and logic bugs in Solidity contracts, flash loan-driven oracle manipulation, eclipse attacks on peer-to-peer networking layers, selfish mining and time-bandit attacks on consensus, sandwich attacks and MEV extraction on transaction ordering, governance attacks on DAOs, and private key compromise through social engineering and supply chain infiltration. Detection and mitigation strategies exist for each attack class, but they are scattered across disparate research communities – smart contract auditing, network security, consensus theory, and applied cryptography – with no unified framework for evaluating which strategies are effective against which attack classes under which network conditions. We present the Blockchain Attack Detection and Mitigation Framework (BADMF), evaluating eight detection strategies and six mitigation strategies across seven attack classes on four blockchain platforms (Ethereum, Solana, Hyperledger Fabric, Cosmos). Our Detection-Mitigation Effectiveness Score (DMES) measures detection accuracy, detection latency, false positive rate, mitigation coverage, and mitigation deployment cost. Machine learning-based anomaly detection achieves the highest detection accuracy (F1 = 0.934) across attack classes, while formal verification provides the strongest pre-deployment mitigation (preventing 94.2% of smart contract vulnerabilities before mainnet deployment).