A Collaborative Anomaly Detection Model Using En-Semble Learning and Blockchain

Intrusion Detection Systems (IDS) have historically been constructed using a centralized topology in which a single device monitors the whole network. However, as the complexity and scope of contemporary networks have grown, this strategy has become less successful. Centralized intrusion detection systems might suffer from poor performance, restricted scalability, and vulnerability to specific assaults. To solve these constraints, there is a rising demand for collaborative intrusion detection systems (IDS) that can share workload among numerous devices and better manage large-scale networks. Collaboration allows intrusion detection systems to identify breaches more efficiently by integrating and analyzing data from numerous sources. The use of blockchain technology is critical to attaining a collaborative IDS. Blockchain enables the safe, decentralized storage and sharing of information between multiple devices, which is crucial for establishing trust and preserving the system's integrity. Furthermore, machine learning (ML) methods may be utilized to enhance the effectiveness of intrusion detection systems by recognizing new and emerging threats. ML may aid in the detection and response to network traffic patterns and abnormalities, allowing the system to detect and respond to assaults more efficiently. A dependable and scalable detection system may be created by integrating these techniques. The collaborative intrusion detection system (IDS) based on blockchain technology and ML algorithms can increase the precision and effectiveness of identifying network intrusions while preserving system security and integrity.