This is the project on ransomware detection using machine learning technique.

In our experiments, malware pcaps were referenced from Malware-traffic-analysis

1. Python 2.7
2. dpkt
3. sklearn

We gathered 155 different Ransomwares from Feb.2015 to Sep.2016, and seperated them into 7 main families:

1. cerber:9
2. CrypMic:28
3. Cryptowall:34
4. CryptXXX:34
5. Locky:16
6. Teslacrypt:22
7. Other:16

We use most three families for experiments: CryptMic, Cryptowall, and CryptXXX

1. Put your ransomware pcap files in malware\_pcap and normal\_pcap
2. Run start.sh to extract http headers from pcap files

Use pcap_Parser -p if you want to parse TCP payloads

We use PCA to reduce dimensions of initial payloads.

python ./visual/pca.py [extracted_data]

It will produced a pickle file, and then use

python ./visual/show.py [pickle_file]

This will show the structure of those payloads.

After PCA as pre-training, we could use those principle components to fit in different ML models. As an example, we use SVM in sklearn to classify those payloads.

python ./model/svm.py [pickle_file]

The 5-folds cross validation results of Cryptmic are

SVM (C=1, linear kernel)

fold-1: 0.82227159
fold-2: 0.83194444
fold-3: 0.815
fold-4: 0.82361111
fold-5: 0.82272548

Our Results are published on IEICE IA 2016