Artificial Neural Networks and Enhanced Adam Optimization for Effective Wi-Fi Intrusion Detection

In recent years, wireless network expansion has been astounding. The upsurge in the mobility of smartphones and wireless self-contained devices through Wi-Fi network access. These devices have evolved into quintessential electrical gadgets. As wireless networks have gained popularity, they have become increasingly susceptible to attacks. Achieving a high rate of detection, accuracy, and the least false positive result is imperative for network intrusion detection systems. We present a method for training artificial neuron networks with an enhanced Adam optimization (EAO) algorithm to detect intrusions against Wi-Fi networks efficiently. The validation of the method’s performance was done by comparing its results with those of other optimization algorithms and machine learning techniques on a publicly available Aegean Wi-Fi intrusion dataset. With its outstanding performance, this Wi-Fi intrusion detection system, which utilizes artificial neural networks and the enhanced Adam optimizer, offers a superior alternative for safeguarding Wi-Fi networks.