IoT Network Vigilant: A Hybrid Framework for Efficient Prediction of Robust IoT Network Intrusion Detection Using FPA-BiNN
DOI:
https://doi.org/10.24237/djes.2025.18408Keywords:
IoT Security, Intrusion Detection, Two Phase Flower Pollination Algorithm, Binarized Neural Networks , Novel Feature EngineeringAbstract
The number of Internet of Things (IoT) devices is increasing rapidly, with estimates predicting more than 41 billion devices by 2025. This growth has also expanded the attack surface, making IoT networks highly vulnerable to cyberattacks. Traditional intrusion detection systems are not suitable for IoT because they depend on known attack signatures, require high computational power, and many false alarms. These limitations make them difficult to deploy on resource-constrained edge devices. The aim of this study is to develop a lightweight and accurate intrusion detection framework specifically designed for IoT environments. We introduce IoT Network Vigilant, a hybrid framework improves intrusion detection performance while remaining efficient enough for real-time use. The framework consists of three key parts. First, we design 27 new IoT-specific features that capture device behavior, traffic asymmetry, and temporal patterns. Second, we apply a two-stage Flower Pollination Algorithm (FPA) to select the most useful features. The first stage ranks features using mutual information, and the second stage removes redundant features using correlation analysis. This process reduces the dataset size by about one-third. Third, we employ Binarized Neural Networks (BNNs), which use binary weights and activations, allowing fast and low-power classification. The model is tested on the IoTID20 dataset, and class imbalance is handled using SMOTE. The results show strong performance, with 98.43% accuracy, 99.03% precision, and 97.32% recall. These scores represent a 4.5% improvement in accuracy compared with existing methods. Overall, this framework offers a robust, efficient, and deployable intrusion detection solution for modern IoT networks.
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Copyright (c) 2025 R.M. Alamelu, J.Christy Eunaicy, T.S.Urmila, C. Jayapratha, J.Naveen Ananda Kumar, G.B.GovindaPrabhu, R. Mahalakshmi Priya
This work is licensed under a Creative Commons Attribution 4.0 International License.
