Resilient IoT Security: Early Flood Attack Detection in IoT Networks Using GRU Deep Learning Model

Securing Internet of Things (IoT) networks has become increasingly critical as their integration across essential sectors continues to expand. Among the most pressing threats are flood attacks, a form of Distributed Denial of Service (DDoS) that overwhelms network resources and causes service degradation. In this study, the detection of flood attacks in IoT environments is addressed using a deep learning model based on the Gated Recurrent Unit (GRU) architecture. Within the scope of the analysis, the CICIoT2023 dataset, which reflects realistic IoT traffic and attack behavior, was employed for training and validation. The results have shown that the flood attacks were successfully detected, and the model achieved an accuracy score of 0.98, with moderate precision, recall, and F1 scores. In this way, flood attacks in IoT can be identified early to mitigate their impact and enhance the resilience of IoT infrastructure. This study contributes to intelligent IoT security by integrating updated datasets, sequential modeling, and empirical evaluation, establishing a solid foundation for future research in threat detection systems.