The application of machine learning and deep learning to intrusion detection systems (IDSs) enhances their ability to detect and respond to sophisticated cyber threats efficiently and effectively, providing a robust defense mechanism in the ever-evolving landscape of cybersecurity. However, many environments where IDSs are deployed, such as IoT devices or edge computing nodes, have experienced several challenges with limited computational power, memory, and storage. In this paper, we leverage Explainable Artificial Intelligence (XAI) for feature selection and Knowledge Distillation (KD) to improve performance and decrease the complexity of the model for limited-resource devices. Specifically, XAI can identify features with little to no impact on the predictions, which can be removed to reduce the dimensionality of data and simplify the model. Besides, we leverage the KD technique to transfer the knowledge from a larger model to a small one to obtain a lightweight yet powerful IDS. Moreover, a new optimizer is also utilized to achieve high accuracy and robustness, while maintaining the speed and efficiency necessary for real-time intrusion detection. The experiments on three common IDS datasets, including CIC-IDS2017, UNSW-NB15, and NSL-KDD, prove our proposed method’s effectiveness with high accuracy.