Blockchain and Machine Learning-Based Hybrid IDS to Protect Smart Networks and Preserve Privacy

The cyberspace is a convenient platform for creative, intellectual, and accessible works that provide medium expression communication. Malware, phishing, ransomware, distributed denial-of-service attacks pose threat to individuals organisations. To detect predict cyber threats effectively accurately, an intelligent system must be developed. Cybercriminals can exploit Internet of Things devices endpoints because they are not have limited resources. A hybrid decision tree method (HIDT) proposed in this article integrates machine learning with blockchain concepts anomaly detection. In all datasets, the predicts shortest amount time has highest attack detection accuracy (99.95% KD99 dataset 99.72% UNBS-NB 15 dataset). ensure validity, binary classification test results compared those earlier studies. HIDT’s confusion matrix contrasts previous models by having low FP/FN rates high TP/TN rates. By detecting malicious nodes instantly, reduces routing overhead lower end-to-end delay. Malicious detected instantly network within short period. Increasing number leads higher throughput, throughput measured at 50 nodes. performed well terms packet delivery ratio, delay, robustness, scalability, demonstrating effectiveness system. Data protected from system, which used governments businesses improve security resilience.