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The rapid expansion of the Internet of Things (IoT) has increased security concerns, thereby necessitating efficient intrusion detection systems (IDS). In this paper, we propose a real-time IoT IDS designed by combining a random forest (RF) classifier with an ensemble feature selection technique (EFST). The proposed IDS can be deployed on a small-scale field-programmable gate array (FPGA) board. The system utilizes a two-metric ensemble feature selection process to reduce computational complexity and enhance classification accuracy. In addition, the EFST aggressively extracts a limited number of features, thereby reducing the complexity of the RF model. Then, the tailored RF classifier is mapped onto an FPGA-based hardware accelerator to realize real-time detection. The proposed method was evaluated experimentally on the benchmark BoT-IoT dataset. The results demonstrate that the proposed IDS realizes significant improvements in terms of resource utilization and processing time compared to several state-of-the-art FPGA-based IDS implementations while maintaining sufficient detection accuracy. In particular, our implementation on the Xilinx PYNQ Z2 achieved 10.2×, 135.7×, and 8.43× speed-up compared to state-of-the-art IDSs running on an Intel Core i7 CPU, an ARM Cortex-A9 microprocessor, and a neural network-based accelerator on the PYNQ, respectively. In addition, our approach exhibits the lowest resource utilization among FPGA-based IDS solutions. These results demonstrate that this work contributes to developing secure and sustainable IoT ecosystems by integrating EFST, RF classification, and FPGA-based acceleration.
Zeng et al. (Fri,) studied this question.