Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection
  Model
Khushnaseeb Roshan Aasim Zafar
Anomaly detection and its explanation is important in many research areas
such as intrusion detection, fraud detection, unknown attack detection in
network traffic and logs. It is challenging to identify the cause or
explanation of why one instance is an anomaly? and the other is not due to its
unbounded and lack of supervisory nature. The answer to this question is
possible with the emerging technique of explainable artificial intelligence
(XAI). XAI provides tools and techniques to interpret and explain the output
and working of complex models such as Deep Learning (DL). This paper aims to
detect and explain network anomalies with XAI, kernelSHAP method. The same
approach is used to improve the network anomaly detection model in terms of
accuracy, recall, precision and f score. The experiment is conduced with the
latest CICIDS2017 dataset. Two models are created (Model_1 and OPT_Model) and
compared. The overall accuracy and F score of OPT_Model (when trained in
unsupervised way) are 0.90 and 0.76, respectively.