Bird-inspired optimization approach using taper-shape transfer function for intrusion detection in IoT networks

The Internet of Things (IoT) has emerged as a pervasive technological paradigm that interconnects heterogeneous devices and sensors, enabling continuous data acquisition, communication, and intelligent decision-making. However, the large-scale, dynamic, and heterogeneous nature of IoT environments introduces significant cybersecurity threats, making intrusion detection a critical component of IoT network protection. The complexity and high dimensionality of IoT traffic data pose substantial challenges for machine-learning-based intrusion detection systems, particularly for classification accuracy. In this context, feature selection (FS), which aims to identify the most informative and non-redundant features, plays a vital role in enhancing detection performance. This study proposes a model to investigate the FS problem using bird-based metaheuristic optimization algorithms, integrated with a taper-shaped transfer function for binary transformation. The proposed framework aims to identify the most informative and non-redundant features from high-dimensional IoT datasets to enhance classification performance. The kNN, SVM, and RF classifiers are employed to evaluate the model using 10-fold cross-validation. Experimental results on the RT-IoT2022 and IoTID20 datasets show that bird-based FS methods achieve substantial dimensionality reduction and strong classification performance. The Secretary Bird Optimization Algorithm (SBOA), the best-performing model on the RT-IoT2022, identified only 6 of 81 features, achieving the highest feature reduction ratio of 92.59% and a classification accuracy of 99.69%. Moreover, the algorithm selected only 7 of 81 features, achieving a feature reduction ratio of 91.36% and a classification accuracy of 98.46% on the IoTID20 dataset. Additionally, SBOA performs well in terms of sensitivity, specificity, precision, and computational time, underscoring its robustness in handling complex IoT traffic data. The findings indicate that bird-inspired optimization approaches, when integrated with an effective binary transfer mechanism, offer a powerful solution for real-time IoT intrusion detection systems.