An artificial neural network and API to detect Windows malware, based on Ergo and LIEF.

Installation

cd /path/to/ergo-pe-av
sudo pip3 install -r requirements.txt

Use as an API

ergo serve /path/to/ergo-pe-av --classes "clean, malware"

From the client, to scan a file that the server can access too:

curl "http://localhost:8080/?x=/path/to/file.exe"
# or
curl --data "x=/path/to/file.exe" "http://localhost:8080/"

To upload the whole file:

curl -F "x=@/path/to/file.exe" "http://localhost:8080/"

To encode a file to a vector of raw features:

curl -F "x=@/path/to/file.exe" "http://localhost:8080/encode"

To scan a vector of raw features:

curl --data "x=0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.847058823529,......" "http://localhost:8080/"

Model Statistics

The dataset is made of ~200000 samples divided in two subfolders:

• classes/pe-malicious with 100000 malware samples from VirusTotal
• classes/pe-clean with 100000 clean samples

The dataset.csv training file has been generated with:

ergo encode ergo-pe-av /media/evilsocket/4TB/datapath-pe/classes --filter "*.exe"

Training	ROC/AUC

Training	Validation	Testing