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Achieve user authentication by applying deep learning techniques to the wireless signals generated by mobile devices
Project mission
Project mission
Use of WiFi signals to capture inherited behavioral characteristics to facilitate identification/authentication.
Environment Dependent Solution (Robust to Placement).
To examine CSI of WiFi to study behavioral characteristics.
Develop a CNN model resilient to spoofing attacks.
The Team
Aditi Satish
Aditi Satish
Daniel Liu
Daniel Liu
Sharad Prasad
Sharad Prasad
Emily Gao
Emily Gao
David Man
David Man
WEEKLY PROGRESS
Week 1
Week 1
- Read the MASS paper from Winlab that uses deep learning techniques to perform user authentication
- Weekly Presentation Link
Week 2
Week 2
- Read the MobiCom paper from our lab that uses wireless signals to perform activity recognition
- Learnt programming tools for deep learning. (Python and Tensorflow).
- Weekly Presentation Link
Week 3
Week 3
- Overview of Tensorflow tools- followed the tutorial.
- Tutorial used the Fashion MNIST dataset, which has 70,000 grayscale images in 10 categories. Each item was shown in a low resolution image of 28x28 Pixels.
- Reviewed MobiCom Paper "E-eyes: Device-free Location-oriented Activity Identification Using Fine-grained WiFi Signatures".
- Weekly Presentation Link
Week 4
Week 4
- Continued to familiarize ourselves with the Python packages (Tensorflow, Scipy).
- Obtained CSI data and began preprocessing.
Week 5
Week 5
- Study the CSI data.
- Preprocess Wifi CSI data (denoising and segmentation) with SciPy.
- Study ways to segment CSI data using moving variance.
Week 6
Week 6
- Continued processing the CSI data with SciPy and Pandas library.
- Begin programming the CNN and deep learning model with Tensorflow
Week 7
Week 7
- Finish segmenting activities from CSI data.
- Record CSI data of different activities/behaviors (squatting, sitting).
- Convert time series data to frequency domain utilizing Fourier transform.
- Continue tuning feature extraction model.
Week 8
Week 8
- Collect more data to be used as inputs for models (different activities, multiple users and antennas).
- Implement user and activity recognizer models.
- Continue developing & hyperparameter tuning the deep learning to boost validation accuracy
Week 9
Week 9
- Finish developing & hyperparameter tuning the deep learning to boost validation accuracy
- Finish developing project page
- Complete Open House Presentation
Week 10
Week 10
Code
Data Segmentation: https://colab.research.google.com/drive/16djK6L4wWOSM1DxzcM62u_KcBZ35M0Vn#scrollTo=rAQAaoh7pX1Y
Deep Learning Model: https://colab.research.google.com/drive/1RpMG24kZFY3AqP6hpyGlnXQthnBcdiTI?usp=sharing
Spectrogram: https://colab.research.google.com/drive/11ea81W1FDMeg7oiNGX3EH71Mk8wZZbr2?usp=sharing
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