Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model

XAI helps answer "why" questions in security: The paper demonstrates that explainable AI techniques like Kernel SHAP can identify why a particular network event is flagged as anomalous versus normal, addressing a critical gap in traditional anomaly detection systems that often provide alerts without explanation.

Explainability can improve model performance: By using Kernel SHAP to understand and optimize their model, the researchers achieved an F-score of 0.76 and 90% accuracy in unsupervised network anomaly detection, suggesting that the process of explaining model decisions can simultaneously enhance detection quality.

XAI is particularly valuable for security applications: The paper emphasizes that anomaly detection in intrusion detection, fraud detection, and unknown attack scenarios benefits significantly from explainability, since security teams need to understand what triggered an alert to respond appropriately and investigate threats.

Explainability addresses the "black box" problem in deep learning: The research shows that XAI methods can make complex deep learning models interpretable, which is especially important for high-stakes security decisions where stakeholders need transparency about how automated systems classify threats.

Practical implementation is feasible: The study demonstrates that Kernel SHAP can be practically applied to real-world network security datasets (CICIDS2017), suggesting that explainable anomaly detection is not just theoretical but implementable in actual security operations.