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.

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries, especially in light of the growing number of cybersecurity threats. A major and ever-present threat is Ransomware-as-a-Service (RaaS) assaults, which enable even individuals with minimal technical knowledge to conduct ransomware operations. This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models. For this purpose, the network intrusion detection dataset “UNSW-NB15” from the Intelligent Security Group of the University of New South Wales, Australia is analyzed. In the initial phase, the rectified linear unit-, scaled exponential linear unit-, and exponential linear unit-based three separate Multi-Layer Perceptron (MLP) models are developed. Later, using the combined predictive power of these three MLPs, the RansoDetect Fusion ensemble model is introduced in the suggested methodology. The proposed ensemble technique outperforms previous studies with impressive performance metrics results, including 98.79% accuracy and recall, 98.85% precision, and 98.80% F1-score. The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLP models. In expanding the field of cybersecurity strategy, this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.