Intrusion Detection and Prevention Based on Ensemble Machine Learning and Blockchain Mechanism

Intrusion Detection Systems (IDS) play a crucial role in protecting computer networks from malicious activities. However, traditional IDS approaches often struggle to achieve satisfactory detection performance due to the increasing complexity and diversity of modern security threats. This paper proposes an ensemble learning-based intrusion detection method to address this challenge. By leveraging an ensemble approach, the proposed method combines the strengths of multiple machine learning techniques to enhance detection accuracy and robustness. The framework encompasses data preprocessing, feature selection, model design, and performance evaluation to optimize IDS effectiveness. Feature selection techniques are applied to identify the most relevant attributes, improving model efficiency and interpretability. Additionally, a blockchain-based mechanism is integrated to prevent attacks by ensuring the integrity of network flow data. Experimental results validate the effectiveness of the proposed IDS in terms of detection accuracy, feature significance, and real-world applicability. The proposed ensemble learning approach, coupled with blockchain security, provides a resilient and adaptive solution to counter the evolving landscape of cyber threats, enhancing the security and reliability of computer networks.