A review on intrusion detection system for distributed network based on Machine Learning

Abstract

The distributed mobility management (DMM) in ID/locator separation architectures has recently received extensive attention to provide load-balanced and scalable mobility services. Since IDs cannot be aggregated, it has limitations in terms of network mobility (NEMO) support. The benefits of decentralized systems over centralized systems along with security issues and distributed systems' challenges form the basis of current work. A wider perspective has been envisioned while examining various intrusion detection strategies and potential applications of blockchain technology, which has drawn significant interest from both academia and business about supply chain management, open banking, online payment, and other areas. The processing of IDSs involves integrating blockchain technology to improve Collaborative IDSs (CIDSs). Blockchain's decentralized and tamper-resistant data storage enables secure information sharing among CIDS nodes without a central authority. This enhances detection and response capabilities, and blockchain also facilitates post-incident analysis and forensics.

URN:NBN:sciencein.jist.2024.v12.739