The rapid proliferation of Internet of Things (IoT) devices has introduced unique security challenges, necessitating efficient and lightweight intrusion detection systems tailored to resource-constrained environments. Traditional intrusion detection systems (IDS) often fall short when applied to IoT networks due to their computational complexity and inefficiency in handling high-dimensional data. To address these challenges, this work proposes a lightweight and high-performance intrusion detection framework specifically optimized for IoT networks. The main contribution lies in the integration of the Fisher Score feature selection method combined with a top-k dimensionality constraint, enabling improved model interpretability and classification performance while preserving computational efficiency. Moreover, a unified sampling strategy is employed to address class imbalance in two major IoT botnet variants, Mirai and Gafgyt. The proposed system is thoroughly evaluated using the N-BaIoT dataset, where it achieves an accuracy of 99.92% with the Decision Tree classifier, while other models such as AdaBoost, Gradient Boosting, and Random Forest also demonstrate strong performance. Furthermore, the system’s time efficiency and model simplicity support its feasibility for real-time deployment in practical IoT environments.