HAMI: Hybrid Adaptive AutoML for IoT Intrusion Detection

the growth of IoT devices presents significant security challenges due to their diverse and dynamic environments. To address this, we developed HAMI, a novel intrusion detection system. Our method leverages the GIFS algorithm, which combines feature rankings from ensemble models with genetic algorithms to uncover subtle inter-feature correlations, enhancing detection accuracy. Additionally, we introduce GridSearchSMOTE, a technique that optimizes data balancing by evaluating various SMOTE variants through a meticulous grid search, ensuring optimal performance for each dataset. Empirical evaluations of HAMI show superior accuracy, achieving a 99% detection rate and reduced false positives, outperforming existing systems. These results underscore HAMI’s potential to significantly improve IoT network security by exploring its scalability and real-time detection capabilities.