Intrusion and Attack Detection for 5G Networks using Deep Learning Techniques

Abstract: A Wireless Intrusion Detection System is an important part of any system or company connected to the internet and has a wireless connection inside it because of the increasing number of internal or external attacks on the network. These WIDS systems are used to predict and detect wireless network attacks such as flooding, DoS attack, and evil- twin that badly affect system availability. Artificial intelligence (Machine Learning, Deep Learning) are popular techniques used as a good solution to build effective network intrusion detection. That's because of the ability of these algorithms to learn complicated behaviors and then use the learned system for discovering and detecting network attacks. In this work, we have performed an autoencoder with a DNN deep algorithm for protecting the companies by detecting intrusion and attacks in 5G wireless networks. We used the Aegean Wi-Fi Intrusion dataset (AWID). Our WIDS resulted in a very good performance with an accuracy of 99% for the dataset attack types: Flooding, Impersonation, and Injection.