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

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 increasing number internal external attacks on network. These WIDS systems are used predict detect network such as flooding, DoS attack, evil- twin that badly affect availability. Artificial intelligence (Machine Learning, Deep Learning) popular techniques good solution build effective intrusion detection. That's ability these algorithms learn complicated behaviors then use learned for discovering detecting attacks. In this work, we have performed autoencoder with DNN deep algorithm protecting companies by in 5G networks. We Aegean Wi-Fi dataset (AWID). Our resulted very performance accuracy 99% attack types: Flooding, Impersonation, Injection.