These days, in order to obtain access to highly secure, confidential, and private data, hackers penetrate the majority of networks in the current world. Intrusion detection system is frequently employed in network security to prevent network compromise by hackers. This research introduces a blockchain-based collaborative intrusion detection system aimed at enhancing network security by preventing unauthorized access to secure data. By using blockchain technology, the system ensures decentralized, tamper-resistant data sharing of attack signatures between nodes in a distributed network. The system leverages anomaly detection techniques to improve processing speed and detection accuracy. This paper presents a promising approach for privacy-sensitive applications. The Quorum blockchain platform, known for its privacy-focused design, is integrated with machine learning algorithms, specifically the disagreement-based semi-supervised learning method, to enhance performance. This approach balances security and performance, offering promising solution for privacy-sensitive applications by combining the strengths of CIDS, blockchain, and advanced machine learning techniques.