Identifying and Mitigating Device Vulnerabilities: A Statistical Approach to Upgrade Analysis

The quick proliferation of Internet of Things (IoT) devices in many different fields has made security management and device maintenance extremely difficult. Cyber attacks are made more likely by outdated hardware and unpatched software flaws, and current upgrading procedures are still primarily manual and reactive. This study presents a simulation-driven, automated method for proactively assessing if IoT devices require upgrades. This study presents a simulation-driven, automated method for proactively assessing if IoT devices require upgrades. To identify security threats, the framework incorporates Common Vulnerabilities and Exposures (CVE) data and simulates various device setups using Python-based simulations. Prioritized update suggestions are produced by rule-based statistical studies that assess software vulnerabilities, hardware obsolescence, and patch compliance. While Matplotlib visualizations offer decision-makers clear, actionable insights, a user-friendly graphical interface created with Tkinter enables involvement easy. In along with enhancing security compliance and operational efficiency, the system also acts as a teaching tool for cybersecurity research. In order to further increase the robustness and manageability of extensive IoT deployments, future improvements will include real-time monitoring and machine learning-based predictive maintenance.