Penetration Testing and Machine Learning-Driven Cybersecurity Framework for IoT and Smart City Wireless Networks

Abstract

The Internet of Things (IoT) technology development speed, along with its integration into smart city infrastructure, requires strong cybersecurity solutions to protect important systems. The research introduces an extensive framework to protect IoT systems and wireless networks through the integration of penetration testing method with anomaly detection and predictive modeling techniques. In this study, we collected real-time network traffic data as part of our methodology before performing penetration tests with Airmon-ng and Wireshark to create enriched attack scenario datasets. Anomalies were identified using an optimized Isolation Forest model, revealing patterns such as unusual activity involving the Tenda_476300 WiFi network. The Tenda_476300 network exhibited frequent authentication packet anomalies, along with other potential misconfiguration or vulnerability indicators. Predictive modeling utilized both logistic regression (LR) and support vector machines (SVM) for binary classification to identify benign from malicious traffic, resulting in high accuracy rates and precise results. XGBoost achieved better performance than Random Forest (RF) across all metrics when performing as a multiclass classifier to identify Denial of Service (DoS), Distributed Denial of Service (DDoS), and brute force attacks. The reliability and robustness of the constructed models were tested using precision, recall, F1 scores, ROC curves, and precision-recall curves during performance evaluation. The anomaly detection and predictive modeling analysis proves that real-time surveillance systems should incorporate these techniques for proactive security threat discovery and defense. The proposed framework delivers a flexible solution for protecting IoT and smart city wireless networks, which helps create safer, resilient urban environments.