Wi-Fi security in universities

Abstract

As universities increasingly depend on wireless networks for academic, research, and administrative functions, ensuring secure and stable Wi-Fi access has become a priority. These networks, which accommodate thousands of users and devices daily, are frequent targets for cyberattacks. Basic security measures like encryption, firewalls, and password protection provide a bare minimum of security but are typically insufficient against advanced and changing threats.With ever-changing university networks, enhanced security mechanisms are required. This paper expounds on how university Wi-Fi security can be improved using machine learning through enhanced real-time threat detection and response. It specifically discusses the potential of two-stage intrusion detection systems with Explainable Artificial Intelligence (XAI) for improved threat detection and blocking. In addition, deep learning technologies such as Deep Neural Networks (DNN) and Stacked Autoencoders (SAE) are tested on their effectiveness for identifying malicious activities and optimizing network security. With these new technologies, universities and colleges can establish a stronger cybersecurity platform, securing reliable and protected wireless access to their academic communities.