WiAU : An Accurate Device-free Authentication System with ResNet

1. WiAU: An Accurate Device-free Authentication System with ResNet Chi Lin*†, Jiaye Hu*†, Yu Sun*†, Fenglong Ma‡, Lei Wang*†, and Guowei Wu*† * School of Software Technology, Dalian University of Technology, Dalian, 116023, China †Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, 116621, China ‡Department of Computer Science and Engineering, University at Buffalo, NY, 14221, USA

2. Contents • BACKGROUND • SYSTEM DESIGN • EXPERIENMENT • CONCLUSION

3. Background • Authenticating human identity Military Science Smart Life Security and Protection Authenticating human identity is a key technique in pervasive computing and human-computer interactions.

4. Background • WiFi Channel State Information (CSI) The ubiquitous and fine-grained features of WiFi signals make it promising for achieving device-free authentication.

5. Related Work Activity Localization Gesture • Human Authentication Based On WiFi CSI • FreeSensePCA+KNN • WiFiU PCA+SVM • SUA DNN

6. Problems & Scheme • Low accuracy for illegal recognition. • Segmenting activities based on experience. • Unsuitable for environmental dynamics. • Long recognition delay. • Our Scheme: WiAU

7. Contents • BACKGROUND • SYSTEM DESIGN • EXPERIENMENT • CONCLUSION

8. Challenges • Challenges: • Ubiquitous identification without additional equipment. • Flexible and scalable for device-free recognizing/authenticating. • Fast & accurate authentication with noise avoidance.

9. System Overview • Data Collection Continuous raw CSI streams are collected and then recorded into the CSI pool. • Preprocessing A preprocessing module is developed to denoise, segment, select, and mark the featured CSI data. • Identification Authentication/recognition process is carried out, in which ResNet is used to handle preprocessed CSI data for accurate identity classification.. Figure: System Overview

10. Preprocessing • Basic Ideas: • Observations: • Variations reflected by activities are larger than intervals. • Activities are caught by most subcarriers simultaneously.

11. Preprocessing • ASA Segmenting activities automatically. • Iteration First, we transform the CSI stream as, We define a dynamic threshold, . Adjust the threshold, until it satisfy Figure: pseudocode of ASA

12. Preprocessing • Overall Process To process all of the CSI amplitude data streams, we should find a set which can represent the total detected activity intervals. • Process To select a small fraction of representative subcarriers, we use SVD method, Calculate the union set of these segmented time interval sets. Figure: the result of ASA based on a CSI stream

13. Identification • Target Recognition/authentication of human identity. • Model design Generate a new feature vector : Figure: Model Overview . Loss Function: Table: Structure of the convolution module

14. Identification • Transfer Learning To guarantee the robustness of system against environmental dynamics, the transfer learning capability is also our concern. • Design After training task , we can get parameters Θ() satisfying: to train the task , we do not need to initialize parameters but directly tune the parameters obtained from as:

15. Contents • BACKGROUND • SYSTEM DESIGN • EXPERIENMENT • CONCLUSION

16. Experiments • Experiment Scenes • Experiment 1 Identity Authentication with Continuous Activities • Experiment 2 Activity Recognition Scene (a) A office • Experiment 3 Measure the transfer learning ability of our system. Scene (b) Corridors

17. Results • Confusion Matrix Confusion matrix: (a) identifying human in the office (b) identifying human in the corridors, and (c) recognizing activities.

18. Results • Accuracy Comparison Accuracy comparison among WiAU, FreeSense, and WiFiU in: (a) identifying human in the office (b) identifying human in the corridor, and (c)recognizing activities.

19. Other Results • Illegal Recognition & Transfer Learning Comparison of among WiAU, FreeSense, and WiFiU in: (a) illegal recognition accuracy and (b) the top-7 transfer learning abilities. • Recognition Delay

20. Contents • BACKGROUND • SYSTEM DESIGN • EXPERIENMENT • CONCLUSION

21. Conclusions • Contributions • We design a new device-free human authentication system, named WiAU, which is developed upon a two-loss function based ResNet algorithm. • We develop a novel partition algorithm (called ASA) to discretize the continuous fluctuating WiFi signals caused by movements of people. • We implement transfer learning technology to alleviate interferences caused by environment. • Future work We will improve the performance of WiAU and extend it to authenticate multiple users simultaneously.

22. WiAU: An Accurate Device-free Authentication System with ResNet Thank You！ Chi Lin*†, Jiaye Hu*†, Yu Sun*†, Fenglong Ma‡, Lei Wang*†, and Guowei Wu*† * School of Software Technology, Dalian University of Technology, Dalian, 116023, China †Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, 116621, China ‡Department of Computer Science and Engineering, University at Buffalo, NY, 14221, USA