Unified framework for sybil attack detection in mobile ad hoc networks using machine learning approach

Independent wireless communication is possible in a "mobile ad hoc network" regardless of any predefined administrative or physical framework. The comprehensive enhancement of services for these networks depends on protecting their interactions. The Sybil attack creates numerous counterfeit identities to disrupt the system's remote functionalities. Implementing a security plan necessitates the establishment of a trust model that delineates the confidence relationships among entities. The trust structure in mobile ad hoc network security has been extensively researched. Mobile ad hoc networks are intrinsically more vulnerable to security breaches than wired networks because of their wireless characteristics. The primary factors contributing to this are energy limitations and security vulnerabilities. A comprehensive methodology has been established to improve the identification of Sybil attacks in MANETs. The system employs two advanced machine learning approaches, Ensemble Regressive Arboretum and AdaBagging, alongside network-feature extraction. Numerous trust models have been developed by integrating AdaBagging and the Ensemble Regressive Arboretum, while most known approaches rely on a singular framework. A Sybil assault transpires when a few numbers of individuals masquerade as numerous peers to obtain unauthorized access to a significant portion of the system. This research employs a machine learning methodology to identify Sybil attacks in MANETs by collecting network metrics such as traffic characteristics, communication patterns, and node activities.