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Open Access

ARTICLE

NeuroChain Sentinel: A Brain-Inspired Anomaly Detection System Using Spiking Neural Networks for Zero-Day Threat Identification in Blockchain Networks

Shoeb Ali Syed1, Zohaib Mushtaq2,*, Akbare Yaqub3, Saifur Rahman4, Muhammad Irfan4, Saleh Al Dawsari4,5,*
1 School of Computer and Information Sciences, University of the Cumberlands, Williamsburg, KY, USA
2 Department of Electrical Electronics & Computer Systems, University of Sargodha, Sargodha, Pakistan
3 Department of Electrical Engineering, FAST, National University of Computer & Emerging Sciences, Lahore, Pakistan
4 Electrical Engineering Department, College of Engineering, Najran University, Najran, Kingdom of Saudi Arabia
5 School of Engineering, Cardiff University, Cardiff, UK
* Corresponding Author: Zohaib Mushtaq. Email: email; Saleh Al Dawsari. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.076869

Received 27 November 2025; Accepted 22 January 2026; Published online 09 April 2026

Abstract

Blockchain networks are under mounting pressure from emerging complex zero-day attacks that cannot be prevented with conventional security measures. In this paper, we introduce NeuroChain Sentinel, a new bio-inspired cybersecurity model based on spiking neural networks for detecting anomalies in a distributed ledger system in real time. The main innovations are: a Temporal Spike Pattern Recognition algorithm for simulating the biological timing of the neural system to detect malicious transaction patterns; a distributed consensus-verification topology combined with blockchain algorithms; and small-scale neuromorphic engineering, resulting in an 87% reduction in computational load over conventional deep neural networks. In contrast to current rule-based or supervised mechanisms that use labeled attack data, NeuroChain Sentinel uses unsupervised learning with spike-timing-dependent plasticity and automatically discovers novel attack vectors, such as smart contract exploits, 51% attacks, and vulnerabilities in consensus mechanisms. An extensive analysis of Ethereum fraud detection data reveals that 99.64% of all data is detected with a 0.8% false-positive (FP) rate, and the Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) value is 0.9999. The Matthews Correlation Coefficient (MCC) is 0.9897. Given these advantages, the existing implementation is tested only against Ethereum transaction information and has not yet been extended to heterogeneous blockchain architectures. The framework will be generalized to many blockchain platforms, scalability in high-throughput environments will be improved, and its robustness against adversarial attacks will be enhanced.

Keywords

Blockchain security; spiking neural networks; zero-day threat detection; neuromorphic computing; anomaly detection; spike-timing-dependent plasticity; distributed ledger technology

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