Strengthening Wireless Network Security: Supervised Machine Learning-Based Intrusion Detection for Enhanced Threat Mitigation

Abstract

With the wireless networks deemed vital to our laced and unetched world, it has become fundamental to safeguard these networks against the various cyber threats that are on the rise. As a research proposal I show concern about the application of a Supervised Machine Learning-Based Intrusion Detection system, making use of Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Logistic Regression (LR) algorithms. The survey begins with the introduction of an intrusive inter-dataset technology that merges two catalytic datasets into a single efficient model that can address both model security and data sensitivity concerns. The evaluation of several machine learning algorithms within the proposed framework is a core component of the analysis. The accuracy measure as well as others such as recall, precision, and F1 score, are applied to evaluate the algorithms' level in detecting intrusions from different datasets.The investigation shows that the intra-dataset routing noticeably improves the detecting modules' efficiency. Notably, the model of MLP algorithm has enhanced recall which in turn shows the enhancement of the positive instance identification. SVM shows increased precision, which accounts for the improved accuracy since correct names of positive cases are produced more often. LR finds it overall enhance the precisions thus attesting to its efficiency in correct deductions. The research documents the multifaceted nature of IDS, prompting the intervention of the machine learning algorithms with ddoswdelcome intrusion detection system. A study which provides a practical guide for network administrators and security professionals hinting on how to select algorithms to meet the distinct security needs in their enterprises. These findings contribute to the open discourse on wireless network security and serve as a base for future study of this engaging research topic in constantly developing field of intrusion detection.