Above is an example of rule-based inference: we highlight the rule with yellow. Any input that satisfies the rule condition will be classified into a target category. Such rules could represent the behavior of the neural networks.
In our recent paper, we propose an input-independent deep learning interpretation framework. We find the neuron activation probability is an intrinsic property of the neural networks and this property could model the decision boundary of the neural networks withount a specific input. Below is a Figure from our paper, where we show the stability of this intrinsic property (for details, read our paper).
Based on this property, we transform the decision mechanism of the neural networks into a series of rule sets without a specific input. The produced rule set could explain the behavior of the target neural networks.
- Android_malware - Derbin Android malware dataset
- data -data for train and test the model
- model -pre-trained model
- Scripts -the python code to generate the rules
- RuleSet -the directory to store the rule set
- Results -the directory to store the results
- Pdf_malware - Benign/malicious PDFs captured from VirusTotal/Contagio/Google provided by Mimicus.
- data -data for train and test the model
- model -pre-trained model
- Scripts -the python code to generate the rules
- RuleSet -the directory to store the rule set
- Results -the directory to store the results
- Binary - Function Entry Identification for Binary Code provided by ByteWeight
- data -data for train and test the model
- model -pre-trained model
- Scripts -the python code to generate the rules
- RuleSet -the directory to store the rule set
- Results -the directory to store the results
source set.sh
- Run Android malware dataset:
cd ./Android_malware
unzip data.rar
unzip model.rar
cd ./Scripts
bash run.sh - Run Pdf malware dataset:
cd ./Pdf_malware
unzip data.rar
cd ./Scripts
bash run.sh - Run ByteWeight dataset:
cd ./Binary
unzip data.rar
cd ./Scripts
bash run.sh
The trained models are provided in each directory (if required). Or you could train your model through train_model.py in each directory.