  The explanation to an AI model's prediction used to support decision making
in cyber security, is of critical importance. It is especially so when the
model's incorrect prediction can lead to severe damages or even losses to lives
and critical assets. However, most existing AI models lack the ability to
provide explanations on their prediction results, despite their strong
performance in most scenarios. In this work, we propose a novel explainable AI
method, called PhilaeX, that provides the heuristic means to identify the
optimized subset of features to form the complete explanations of AI models'
predictions. It identifies the features that lead to the model's borderline
prediction, and those with positive individual contributions are extracted. The
feature attributions are then quantified through the optimization of a Ridge
regression model. We verify the explanation fidelity through two experiments.
First, we assess our method's capability in correctly identifying the activated
features in the adversarial samples of Android malwares, through the features
attribution values from PhilaeX. Second, the deduction and augmentation tests,
are used to assess the fidelity of the explanations. The results show that
PhilaeX is able to explain different types of classifiers correctly, with
higher fidelity explanations, compared to the state-of-the-arts methods such as
LIME and SHAP.
