Requirement

Hardware

1.Four raspberry Pis with a wireless network adapter and an ethernet adapter 2.One laptop with a Linux OS
3.USB Ethernet adaptors
4.Ethernet cable extenders

Software

python 2.7.16
matplotlib 2.2.4 (https://matplotlib.org/)
netfilter 0.6.4 (https://www.netfilter.org/)
numpy 1.16.2 (https://numpy.org/)
pandas 0.24.1 (https://pandas.pydata.org/)
pyzmq 18.0.1 (https://pyzmq.readthedocs.io/en/latest/)
scapy 2.4.2 (https://scapy.net/)
scikit-learn 0.20.3 (https://scikit-learn.org/)
tcpdump 4.9.2 (https://www.tcpdump.org/manpages/tcpdump.1.html)

Setup

1. Physically connect the Raspberry Pis to all PLCs in level0. (USB Ethernet adaptors are used here since Raspberry Pis typically has only one ethernet port.)
2. Edit and run start_lvl0.sh to enable bridge-Netfilter to set up bridges for all Raspberry Pis.
3. Make sure all the Raspberry Pis and the laptop are put onto the same wireless subnet.

Code structure

This project contains three modules:

• Packet collection
• Active learning
• Attack implications

Usage

Data collection

1. For each Raspberry Pi, copy the scripts from the fold 'raspberry_Pi' to the corresponding Raspberry Pis.
2. Edit the ipadress, port number and file names according to yours.
3. Run the script 'raspberry_Pi/tcpdump.sh' to sniff packets in level 0 for different PLC. (You should install the required software for your raspberry and check that bridge works.)
4. Collect corresponding sensor values from the dataset in the meantime.
5. Edit and run the script 'main/extract_bits.py' and 'main/extra_log.py' to generate the feature vectors and sensor values used for training.

Active learning

1. Edit and run script 'main/train.py' to get a pre-training model using the data collected above.
2. For each Raspberry Pi, edit and run the corresponding 'stage_server.py' ,'stage_client.py' and 'stage_AL.py' from the fold 'raspberry_Pi'. (For the sensor value, you can also collect it from the database directly.)
3. In the meantime, edit and run 'main/zmq/main_client.py' , 'main/zmq/main_server.py' and 'main_predict.py' on the laptop. The 'client' and the 'server' scripts are used to pass all the packets captured by each pi to the laptop and send the new packets generated by our active fuzzing algorithm to each pi.The 'main_predict.py' is the implementation of our algorithm and the 'stage_AL.py' is specially designed to capture packets from the network and modify them.