Empowering Digital Resilience: MachineLearning-Based Policing Models for Cyber AttackDetection in Wi-Fi Networks

Abstract In the wake of the Covid-19 pandemic, there has been a significant digital transformation. The widespread use of wireless communication in IoT has posed security challenges due to its vulnerability to cybercrime. The Indonesian National Police's Directorate of Cyber Crime (Dittipidsiber) is expected to play a preventive role in supervising these attacks, despite lacking a specific cyber-attack prevention function. An Intrusion Detection System (IDS), employing artificial intelligence, can differentiate between cyber-attacks and non-attacks. This study focuses on developing a machine learning-based policing model to detect cyber-attacks on Wi-Fi networks. The model analyzes network data, enabling quick identification of attack indications in the command room. The research involves simulations and analyses of various feature selection methods and classification models using a public dataset of cyber-attacks on Wi-Fi networks. The study identifies mutual information with twenty features as the optimal feature reduction method and Neural Network as the best classification method, achieving a 94% F-Score within 95 seconds. These results demonstrate the proposed IDS's ability to swiftly detect attacks, aligning with previous research findings.