Data leakage analysis in wireless networks using Super-vised and Unsupervised Testing

• Autores: Ashraf, Shahzad, Aslam, Zeeshan
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad La Salle
• Repositorio: Revistas - Universidad La Salle
• Lenguaje: inglés
• OAI Identifier: oai:ojs.revistas.ulasalle.edu.pe:article/108
• Enlace del recurso: https://revistas.ulasalle.edu.pe/innosoft/article/view/108; https://doi.org/10.48168/innosoft.s12.a108; https://purl.org/42411/s12/a108; https://n2t.net/ark:/42411/s12/a108
• Nivel de acceso: acceso abierto
• Materia: Supervised testing; unsupervised testing; neural network; wireless networks; Pruebas supervisadas; pruebas no supervisadas; redes neuronales; redes inalámbricas

Due to an increasing number of wireless spectrums, the multiple frequencies are tangling resource management process that results hindrance in operation. In addition, the previous data become vulnerable when reports are received for data leakage enigma. In this situation, it is indispensable to secure the data in the dataset and detect the actual amount of data during resource transformation mechanism in wireless networks. A system as been developed to detect the leaked data using supervised and unsupervised testing technique by conducting simulation in Python. The targeted and actual outcome is obtained which deduced through supervised and undersized testing, the outcome remained 96.03%, and 94.53% respectively.