The field of cybersecurity is moving towards the development of more explainable and robust models, particularly in the areas of graph neural networks and anomaly detection. Researchers are focusing on creating models that can provide verifiable explanations for their predictions, which is essential for building trust in these systems. The use of techniques such as counterfactual explanations, graph neural networks, and ensemble learning is becoming increasingly popular. These approaches have shown promising results in detecting and explaining various types of attacks, including Advanced Persistent Threats and replay attacks. Noteworthy papers in this area include ProvX, which introduces a counterfactual explanation framework for GNN-based security models, and MirGuard, which proposes a robust anomaly detection framework that combines logic-aware multi-view augmentation with contrastive representation learning. Additionally, the paper on Explainable Ensemble Learning for Graph-Based Malware Detection presents a novel stacking ensemble framework for graph-based malware detection and explanation. Overall, the field is moving towards the development of more transparent and reliable models that can provide valuable insights into their decision-making processes.