The evolving behavior of the attacks may affect the decision boundaries of the trained machine learning models. The issue has not been well investigated, especially with hypervisor-based security solutions where virtual machine (VM)’s network artifacts are introspected and analyzed. In this paper, we proposed a sustainable and explainable flow-filtering-based concept drift-driven network intrusion detection approach, called ‘SFC-NIDS’ which introspects network activities by analyzing VM traffic profile. The VM traffic is captured and pre-processed at the hypervisor to extract important network artifacts. The redundant and trivial network flows have been filtered using the proposed gradient descent-based flow filtering mechanism and validated using explainability. SFC-NIDS employs auto-encoders to reconstruct the traffic features to capture additional patterns. Afterward, the 1D-convolution neural network has been employed to learn and detect malicious attack flows. The model’s sustainability is ensured by integrating the drift detection mechanism with the decision model to retrain it with evolving attack patterns. The approach has been validated with virtual network traffic artifacts collected at the hypervisor and provides 98.9% accuracy, 99.03%, and F1-Score. In addition, the approach has also been validated using the KDD99 dataset, showcasing an accuracy of 99.97% and an F1-Score of 99.98%.