AI-Powered Intrusion Detection Systems in Blockchain Networks
DOI:
https://doi.org/10.63345/sjaibt.v1.i3.101Keywords:
Blockchain Security, Intrusion Detection, Graph Neural Networks, Smart Contracts, Federated Learning, Anomaly Detection, Ethereum, Consensus Security, Explainability, PrivacyAbstract
Blockchain networks—public, consortium, and permissioned—promise integrity, transparency, and decentralization, yet they continue to face a shifting landscape of threats across layers: peer-to-peer overlays, consensus, smart contracts, mempools, bridges, and off-chain oracles. Conventional intrusion detection systems (IDS) tuned for enterprise or ISP traffic struggle to capture blockchain–specific semantics such as transaction graphs, validator behaviors, bytecode execution traces, cross-chain flows, and MEV-style manipulations. This manuscript proposes and analyzes a multilayer, AI-powered IDS architecture tailored to blockchain networks. First, we synthesize the state of the art on deep learning for IDS, graph learning over transaction networks, smart-contract vulnerability detection, and federated learning (FL) for privacy-preserving collaboration among heterogeneous nodes. Second, we formalize a design that fuses (i) graph neural networks for address/contract behavior on dynamic transaction graphs, (ii) sequence models over EVM opcode traces for runtime anomalies and contract-level exploits, (iii) temporal models for mempool manipulation and spam/DoS patterns, (iv) validator-telemetry analytics for consensus-layer deviations including selfish mining, and (v) cross-chain risk scoring to detect bridge and arbitrage abuse. We detail features, training objectives, privacy safeguards (secure aggregation, differentially private updates), and explainability (subgraph rationales, opcode saliency). Finally, we discuss evaluation methodology using public ledgers and labeled case corpora (e.g., Ponzi/phishing datasets) and report illustrative results from a pilot study design, along with deployment guidance for miners/validators, L2 sequencers, exchanges, and custodians. Our analysis indicates that AI-powered, graph-centric, and federated IDS can reduce false positives while improving early detection of fraud patterns and validator misbehavior, provided that model and data governance are rigorous and that alerts are verifiable and auditable. We conclude with open challenges—concept drift, adaptive adversaries, data imbalance, privacy–utility trade-offs, and cross-chain observability—and a roadmap for standardizing datasets and benchmarks for blockchain IDS research.
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