Description This paper presents SecuPi, an intelligent and cost-effective network security and monitoring system built using the Raspberry Pi platform. The system is designed to address increasing cybersecurity threats in environments such as educational institutions and small organizations, where the widespread use of Internet of Things (IoT) devices and Bring-Your-Own-Device (BYOD) policies makes networks more vulnerable to attacks. SecuPi integrates real-time network traffic monitoring, hybrid intrusion detection, and automated defense mechanisms into a lightweight and scalable framework. The system combines signature-based detection with machine learning–based anomaly detection to identify malicious activities such as port scanning, brute-force attacks, and abnormal traffic patterns. In addition, SecuPi employs honeypots, automated firewall updates, and Moving Target Defense (MTD) techniques to proactively mitigate potential threats. A secure web dashboard built with Django provides real-time monitoring, log analysis, and multi-factor authentication for improved system management. Hardware-based alerts using ESP32 or Arduino modules offer immediate physical notifications when suspicious activities are detected. The proposed framework demonstrates how low-cost embedded hardware can be effectively used to build an intelligent and scalable cybersecurity solution. SecuPi aims to provide an affordable and practical network defense platform suitable for educational institutions, research environments, and small-scale enterprise networks.
K et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: