<![CDATA[Using edge computing technology, this research aims to develop an Internet of Things (IoT) system that can detect and resolve phishing attacks in real-time. The system enhances cybersecurity by focusing on rapid detection and response to increasingly sophisticated and frequent phishing attacks. It collects and processes data from various sources, including emails, text messages, and network activity logs of IoT devices, enabling more accurate analysis and detection of different types of phishing attacks. The developed phishing detection model demonstrates high performance with 95% accuracy, 93% precision, 94% recall, and an f1-score exceeding 93.5%. Edge computing allows for local data processing, reducing latency and accelerating threat response. This approach also enhances security by eliminating the need to transmit data to a central server, thus minimizing data breach risks during transmission. The system is well-integrated, using secure communication protocols and implementing Zero Trust principles to ensure maximum security at every layer. High-load simulations demonstrate the system's scalability and resilience, proving its ability to handle large data volumes and simultaneous attacks. Ubuntu Core was chosen as the operating system due to its high security and efficiency, crucial for running IoT devices with limited computing resources. The study also emphasizes the importance of increasing user awareness of phishing threats through automated detection and continuous education, creating a holistic approach to phishing risk mitigation. By combining IoT, edge computing, and machine learning technologies, this research contributes significantly to developing effective and efficient cybersecurity solutions to address evolving phishing threats. The findings pave the way for implementing more robust and responsive security systems in an increasingly connected digital era.]]>
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