  Machine learning (ML) and Deep Learning (DL) methods are being adopted
rapidly, especially in computer network security, such as fraud detection,
network anomaly detection, intrusion detection, and much more. However, the
lack of transparency of ML and DL based models is a major obstacle to their
implementation and criticized due to its black-box nature, even with such
tremendous results. Explainable Artificial Intelligence (XAI) is a promising
area that can improve the trustworthiness of these models by giving
explanations and interpreting its output. If the internal working of the ML and
DL based models is understandable, then it can further help to improve its
performance. The objective of this paper is to show that how XAI can be used to
interpret the results of the DL model, the autoencoder in this case. And, based
on the interpretation, we improved its performance for computer network anomaly
detection. The kernel SHAP method, which is based on the shapley values, is
used as a novel feature selection technique. This method is used to identify
only those features that are actually causing the anomalous behaviour of the
set of attack/anomaly instances. Later, these feature sets are used to train
and validate the autoencoder but on benign data only. Finally, the built
SHAP_Model outperformed the other two models proposed based on the feature
selection method. This whole experiment is conducted on the subset of the
latest CICIDS2017 network dataset. The overall accuracy and AUC of SHAP_Model
is 94% and 0.969, respectively.
