The purpose of this work is to develop an intrusion detection system (IDS) that detects and alerts about de-authentication attacks within IoT networks, to improve network security through real time monitoring and alert generation. A virtual test environment was implemented using Ubuntu for monitoring and Kali Linux for simulating de-authentication attacks. Python Scapy Library was used for packet sniffing and analysis. The system was designed to trigger email alerts only after detecting a minimum number of given de-authentication frames, to reduce false alerts. Mobile devices were used to simulate IoT endpoints, allowing us to evaluate the system's effectiveness in a controlled environment. The proposed IDS effectively identified de-authentication attacks and generated alerts with expected accuracy. The email alert system was optimized to reduce false positives by setting a threshold. This approach demonstrated significant improvements in real time detection and alert management than other traditional methods. The results indicate that this method can improve IoT network security by sending timely and accurate alerts. With adjustable alert thresholds, this enhanced intrusion detection system for Internet of Things networks balances enhanced detection sensitivity with lower false positives.