A Real-Time Anomaly Detection Framework for Ethereum Using Rule-Based Logic, GraphSAGE and Consensus-Driven Event Processing

Abstract

Ethereum is one of the key platforms in public blockchain networks. Ethereum used for various critical applications like decentralized finance (DeFi), smart contract based and supply chain operations etc., Because of its growing popularity along with its open and anonymous nature, network can contribute fraudulent or abnormal transactions. The existing anomaly detection methods suffer from real time detection as they use rule based or statistical methods. These methods are failed to identify dynamic or structural transactional anomalies and has no consensus verification. To address these challenges, in this study we designed an anomaly detection model that features rule based logic, Graph Neural Networks with GraphSAGE and Consensus-driven Complex Event Processing (CEP). We developed this model by combining three key procedures primarily the transactions are fetched from live Ethereum network and presented to rule based filtering. Graph construction and feature extraction will be done at second stage in the model to understand structural pattern and detects dynamically evolving abnormal patterns. In final phase consensus verification introduced as novel contribution in this model to reduce false positives. The framework tested for live Ethereum data that shows improvements detection accuracy of GNN model with low latency time. On the whole, the proposed system model supports practical security monitoring in Ethereum networks. This framework efficiently helps in detecting fraud or abnormal transactions that degrades the performance, reliability or accuracy of the decentralized network.