A Novel Intrusion Detection System for Wireless Enterprise Networks using Ensembled Machine Learning Models

Abstract: For contemporary enterprises, the significance of wireless enterprise networks has expanded due to their heightened adaptability and mobility in terms of connectivity and access to information. However, these networks are also vulnerable to various forms of cyber-attacks, including intrusions from external parties, breaches of data, and outbreaks of malware. To counter these threats, it is imperative to possess efficient intrusion detection systems (IDSs). One potential strategy to enhance the performance of IDSs for wireless enterprise networks is the utilization of ensembled machine learning models. To construct an IDS model in a wireless environment employing the AWID dataset, this investigation integrates the prognostications of three distinct classification techniques: specifically, the hybrid model of CNN-SVM, the ensemble model of SVM-MLP, and the ensemble model of DT-KNN. The efficacy of the model is assessed based on the statistical information derived from the confusion matrix, such as accuracy, recall, precision, and F1-scores.

Keywords: Intrusion Detection System (IDS); Machine Learning; Ensemble models; AWID.