Bioluminescent Filament-Inspired AI for Adaptive Smart Contract Intrusion Detection

Abstract

Smart contract environments are increasingly targeted by stealthy, adaptive attacks that evade conventional rule-based or static anomaly detection systems. Inspired by the anglerfish’s bioluminescent filament, which perceives and lures activity in dark, dynamic environments, this research introduces a Bioluminescent Filament-Inspired Artificial Intelligence Perception framework for smart contract intrusion detection. The proposed model emulates biological sensory adaptation through multi-modal attention layers that dynamically illuminate anomalous behaviors in contract execution flows. By integrating self-supervised temporal perception with context-driven feedback, the framework continuously refines its detection sensitivity while maintaining low computational overhead. We evaluate the framework using fuzz-tested smart contract vulnerability datasets that simulate diverse malicious execution behaviors observed in Ethereum environments, demonstrating over 98% detection accuracy with a 40% reduction in latency compared to traditional deep learning-based IDS models. This biologically inspired perception paradigm offers a scalable, energy-efficient solution for securing blockchain-based decentralized systems against evolving threat vectors.