Unauthorized Access Point Detection using Machine Learning Algorithms

Abstract

The increasing cybersecurity threats in the digital landscape, characterized by the persistent attempts of malicious entities to acquire valuable data, underscore the importance of implementing robust defenses to counter unauthorized access. Safeguarding against the relentless pursuit of valuable data by malicious actors is of paramount significance in the digital realm. A key element in this defense strategy involves the use of Unauthorized Access Point Detection Systems (UADPS), which play a crucial role in achieving a balance between high detection accuracy and minimizing false alarms. This document offers a thorough examination of UADPS, presenting a taxonomy of machine learning approaches, such as Support Vector Machine (SVM), K Nearest Neighbors (KNN), and Multilayer Perceptron (MLP), to significantly improve detection capabilities. Through a meticulous evaluation, the paper reveals the nuanced strengths and weaknesses of recent UADPS implementations, highlighting the transformative impact of machine learning algorithms in bolstering information security. The incorporation of methodologies like SVM and KNN proves to be instrumental in addressing cybersecurity challenges. By scrutinizing datasets, performance metrics, and application scenarios, the paper contributes valuable insights to the ongoing conversation on enhancing UADPS accuracy, fostering innovation in cybersecurity practices, and advancing overall network security.