SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability. (arXiv:2210.17376v2 [cs.CR] UPDATED)

Interpretability, trustworthiness, and usability are key considerations in
high-stake security applications, especially when utilizing deep learning
models. While these models are known for their high accuracy, they behave as
black boxes in which identifying important features and factors that led to a
classification or a prediction is difficult. This can lead to uncertainty and
distrust, especially when an incorrect prediction results in severe
consequences. Thus, explanation methods aim to provide insights into the inner
working of deep learning models. However, …

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