Motivation:
The lack of transparency of the deep learning models creates key barriers to establishing trusts to the model or effectively troubleshooting classification errors
Common methods on non-security applications:
forward propagation / back propagation / under a blackbox setting
the basic idea is to approximate the local decision boundary using a linear model to infer the important features.
Insights:
A mixture regression model : can approximate both linear and non-linear decision boundaries
Fused Lasso: a panalty term commonly used for capturing frature dependency.
By adding fused lasso to the learning process, the mixture regression model can take features as a group and thus capture the dependency between adjacent features.
Evaluations:
classifying PDF malware: trained on 10000 PDF files
detecting the function start to reverse-engineer binary code.
Innovation:
Under a black-box setting :
Give an input data instance x and a classifier such as an RNN, identify a small set of features that have key contributions to the classification of x.
Paper Reading——LEMNA:Explaining Deep Learning based Security Applications
原文：https://www.cnblogs.com/xlwang1995/p/10056309.html