Hybrid Collaborative Intrusion Detection System Based on Blockchain & Machine Learning

Abstract

Abstract Intrusion Detection Systems (IDS) have traditionally been designed with a centralized structure, where a single device is responsible for monitoring the entire network. However, with the increasing complexity and scale of modern networks, this approach has become less effective. Centralized IDS can suffer from performance issues, limited scalability, and vulnerability to targeted attacks. To address these limitations, there is a growing need to develop collaborative IDS that can distribute the workload across multiple devices and better handle large-scale networks. Collaboration enables IDS to detect intrusions more effectively by combining and analyzing data from multiple sources. The adoption of blockchain technology is essential in achieving a collaborative IDS. Blockchain provides a secure, decentralized way to store and exchange information between different devices, which is critical for building trust and ensuring the integrity of the system. Furthermore, machine learning algorithms can be used to improve the performance of IDS by detecting new and emerging threats. Machine learning can help to identify patterns and anomalies in network traffic, enabling the system to detect and respond to attacks more effectively. By combining these approaches, a reliable and scalable detection system can be developed. The collaborative IDS using blockchain technology and machine learning algorithms can improve the accuracy and efficiency of detecting network intrusions while maintaining the security and integrity of the system.