This repository is the official implementation of Deep Learning based end-to-end Physical Layer Secret Key Generation using Wireless Channel State Information.
You can find the paper here: link

• Introduction
• Methodology
• Experiment and Datasets
• Usuage
• Results

• By 2030, 500 billion wireless devices will be connected
• Wireless communication is vulnerable to potential attacks from eavesdroppers and requires additional security
• Cryptographic-based key generation is inappropriate for low-cost wireless communications devices due to high computational complexity
• Simplified wireless communication security research using physical layer information (CSI) is required

• The process of key generation using CSI is typically divided into four stages: channel probing, quantization, information reclamation, and privacy amplification
• Quantization is the process of converting channel measurements into binary values, which results in loss of information
• Additional communication between users is required to restore lost information
• Requires end-to-end key generation for additional communication-free key generation

• Converting the gain of these two into binary keys through deep learning models when legitimate users Bob and unjust user Eve are present
• Initially, deep learning has random weights Based on an early basis, the purpose function determines the suitability of the key
• Updates the weight of deep learning by optimizing the objective function through genetic algorithms
• when optimization is over, we use the best deep learning model
• The objective function to be optimized is as follows 
• The resulting characteristics of the generated keys are as follows:

• IEEE 802.11 framework
• 5GHz carrier frequency (60 mm wavelength)
• 80 MHz bandwidth (242 valid subcarriers)
• Fix the position of the Alice and place it linearly at 6 mm intervals from Loc 1 to Loc 27.

• Use gain of the channel state information

To install requirements:

pip install -r requirements.txt

There is two options to training model and evaluate the result.

python main.py --result_save_dir <path-to-dir> --EPOCHS 1000 --N_POPULATION 100 --N_BEST 10 --h1 64 --h2 64 --h3 64 --early_stopping 50 --POWER_RATIO 0.5 --CONST 0.8
python evaluate.py --result_save_dir <path-to-dir> --reference 1 --POWER_RATIO 0.5 --CONST 0.8

or just

sh run.sh

You can use the pre-trained model without re-training from the beginning.
It can be found in 'results' dir and just specify the result_save_dir in evaluate.py argument to it.

python evaluate.py --result_save_dir results --reference 1 --POWER_RATIO 0.5 --CONST 0.8

• Suppose Loc 1 as Bob, Loc 2-27 as Eve
• Assign alpha as 0.8 in objective function

• Metric: Percential Hamming Distance (PHD)

• PHD increases nonlinearly as distance moves away

If there is something wrong or you have any questions, send me an e-mail or make an issue.