V2I8P15

AI Based Intrusion Detection and Prevention System for Securing Wireless Communication Networks

Comfort Gyamea Acka^1*, Abosede Oluwabunmi Adelore², Emmanuel Toba Popoola³, Saeed Hubairik Aliyu⁴, Abdullateef Akorede Ademoye⁵

Abstract

As wireless communication networks become increasingly integral to modern infrastructure, they face a growing threat from sophisticated cyberattacks. This paper explores the development and implementation of AI based Intrusion Detection and Prevention Systems (IDPS) designed to secure wireless networks through intelligent, adaptive threat detection and mitigation. Leveraging advanced artificial intelligence (AI) and machine learning (ML) algorithms, these systems are capable of monitoring real time network traffic, identifying anomalies, and responding proactively to potential intrusions. Techniques such as supervised learning, deep learning, and ensemble methods enable the accurate classification of malicious behaviour, while explainable AI (XAI) helps address transparency and accountability concerns. Despite their promise, AI based IDPS face several challenges, including compliance with data protection regulations, the interpretability of complex models, and performance limitations in resource constrained environments. This paper discusses the current state, applications, challenges, and future directions of AI enabled IDPS, offering a comprehensive framework for enhancing the resilience of wireless communication systems.

Keywords:

Intrusion Detection and Prevention System (IDPS), Wireless Network Security, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Anomaly Detection