Enhancing Explanation of LSTM-Based DDoS Attack Classification Using SHAP With Pattern Dependency

DDoS attacks pose serious threats to the availability and reliability of computer networks. With the increasing complexity of DDoS attacks, the accurate detection and classification of these attacks is essential to ensure the protection of network systems. In this paper, we leverage the power of the LSTM model for DDoS attack classification and its ability to automatically learn complex patterns and select features from raw traffic at the packet level. LSTM models have remarkable performance in network traffic classification, however explaining the internal workings of them remains challenging, which hinders their wider adoption in real-world applications. To address this limitation, we propose the SHAP with Pattern Dependency (SHAPPD) approach to explain the predictions of the LSTM model. The results demonstrate significant performance in classifying the DDoS attacks from raw traffic using the LSTM model. SHAPPD effectively explains the predictions of the LSTM model, highlighting the underlying packet traffic fields that drive the LSTM to make its true and false positive predictions and finding the common fields between the DDoS attacks. The results of the comparison between the SHAPPD and the original SHAP emphasize that the SHAPPD is superior to the original SHAP in providing more elaborative justifications for DDoS attacks classification results. The SHAPPD, by quantifying the contribution of each input feature and considering the interdependencies between the features as well as the continued traffic packets, enables security analysts to gain insights into the decision-making process of the LSTM model and identify critical indicators about the DDoS attacks.