Enhancing Scalability in Intrusion Detection System Through Machine Learning and Blockchain Integration

This study addresses both present and future risks to data privacy by integrating intrusion detection with blockchain technology. The approach seeks to improve data security and detect sophisticated criminal activity by utilizing machine-learning algorithms. The study presents a novel Intrusion Detection System (IDS) that assesses different machine-learning models in a blockchain context, such as Decision Tree, Random Forest, Extra Tree, and XGBoost. Improved data privacy, security, and protection for Internet-of-things networks are the main goals of the system. Ant Colony Optimization and PyCaret increase the accuracy of real-time data analysis. Using datasets like CICIDS2017 and NSL-KDD, the study empirically shows the model's effectiveness and its potential to lower cyber security vulnerabilities. Blockchain and machine learning work together to improve scalability and response times by blocking detected intrusions in addition to analyzing logs and verifying IP addresses. This innovative approach is a major advancement in addressing new cyber threats and improving cyber security defenses.