Abstract
Gesture-based authentication has emerged as a non-intrusive, effective means of authenticating users on mobile devices. Typically, such authentication techniques have relied on classical machine learning techniques, but recently, deep learning techniques have been applied this problem. Although prior research has shown that deep learning models are vulnerable to adversarial attacks, relatively little research has been done in the adversarial domain for behavioral biometrics. In this research, we collect tri-axial accelerometer gesture data (TAGD) from 46 users and perform classification experiments with both classical machine learning and deep learning models. Specifically, we train and test support vector machines (SVM) and convolutional neural networks (CNN). We then consider a realistic adversarial attack, where we assume the attacker has access to real users’ TAGD data, but not the authentication model. We use a deep convolutional generative adversarial network (DC-GAN) to create adversarial samples, and we show that our deep learning model is surprisingly robust to such an attack scenario.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mohit Agrawal, Pragyan Mehrotra, Rajesh Kumar, and Rajiv Ratn Shah. Defending touch-based continuous authentication systems from active adversaries using generative adversarial networks. arXiv preprint arXiv:2106.07867, 2021.
Mohammad Al-Rubaie and J Morris Chang. Reconstruction attacks against mobile-based continuous authentication systems in the cloud. IEEE Transactions on Information Forensics and Security, 11(12):2648–2663, 2016.
Ala Abdulhakim Alariki and Azizah Abdul Manaf. Touch gesture authentication framework for touch screen mobile devices. Journal of Theoretical & Applied Information Technology, 62(2), 2014.
Sukarna Barua, Sarah Monazam Erfani, and James Bailey. FCC-GAN: A fully connected and convolutional net architecture for GANs. arXiv preprint arXiv:1905.02417, 2019.
Debnath Bhattacharyya, Rahul Ranjan, Farkhod Alisherov, Minkyu Choi, et al. Biometric authentication: A review. International Journal of u-and e-Service, Science and Technology, 2(3):13–28, 2009.
Jason Brownlee. Deep learning with Python: Develop Deep Learning Models on Theano and TensorFlow using Keras. Machine Learning Mastery, 2016.
Jason Brownlee. 1d convolutional neural network models for human activity recognition. https://machinelearningmastery.com/cnn-models-for-human-activity-recognition-time-series-classification/, 2020.
Attaullah Buriro, Bruno Crispo, Filippo Delfrari, and Konrad Wrona. Hold and sign: A novel behavioral biometrics for smartphone user authentication. In 2016 IEEE security and privacy workshops, SPW, pages 276–285, 2016.
Xue-wen Chen and Jong Cheol Jeong. Enhanced recursive feature elimination. In Sixth International Conference on Machine Learning and Applications, ICMLA 2007, pages 429–435, 2007.
Chrystian Vieyra. Physics toolbox sensor suite. https://apps.apple.com/us/app/physics-toolbox-sensor-suite/id1128914250, 2016.
Gradeigh D. Clark and Janne Lindqvist. Engineering gesture-based authentication systems. IEEE Pervasive Computing, 14(1):18–25, 2015.
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A. Bharath. Generative adversarial networks: An overview. IEEE Signal Processing Magazine, 35(1):53–65, 2018.
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller. Adversarial attacks on deep neural networks for time series classification. In 2019 International Joint Conference on Neural Networks, IJCNN, pages 1–8, 2019.
Margherita Grandini, Enrico Bagli, and Giorgio Visani. Metrics for multi-class classification: an overview. arXiv preprint arXiv:2008.05756, 2020.
Dennis Guse. Gesture-based user authentication on mobile devices using accelerometer and gyroscope. Master’s thesis, Technische Universität Berlin, 2017.
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R. Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580, 2012.
Elliu Huang. Gesture dataset, 2021. Available from the authors upon request.
Elliu Huang, Fabio Di Troia, Mark Stamp, and Preethi Sundaravaradhan. A new dataset for smartphone gesture-based authentication. https://www.scitepress.org/Papers/2021/104258/104258.pdf, 2021.
Satoru Imura and Hiroshi Hosobe. A hand gesture-based method for biometric authentication. In International Conference on Human-Computer Interaction, pages 554–566, 2018.
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller. Adversarial attacks on deep neural networks for time series classification. arXiv e-prints, pages arXiv–1903, 2019.
Serkan Kiranyaz, Onur Avci, Osama Abdeljaber, Turker Ince, Moncef Gabbouj, and Daniel J Inman. 1d convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing, 151, 2021.
Jiayang Liu, Lin Zhong, Jehan Wickramasuriya, and Venu Vasudevan. uWave: Accelerometer-based personalized gesture recognition and its applications. Pervasive and Mobile Computing, 5(6):657–675, 2009.
Duo Lu, Kai Xu, and Dijiang Huang. A data driven in-air-handwriting biometric authentication system. In 2017 IEEE International Joint Conference on Biometrics, IJCB, pages 531–537, 2017.
Yuxin Meng, Duncan S. Wong, Roman Schlegel, et al. Touch gestures based biometric authentication scheme for touchscreen mobile phones. In International Conference on Information Security and Cryptology, pages 331–350, 2012.
Gautam Raj Mode and Khaza Anuarul Hoque. Adversarial examples in deep learning for multivariate time series regression. In 2020 IEEE Applied Imagery Pattern Recognition Workshop, AIPR, pages 1–10, 2020.
Luis Muñoz-González, Bjarne Pfitzner, Matteo Russo, Javier Carnerero-Cano, and Emil C Lupu. Poisoning attacks with generative adversarial nets. arXiv preprint arXiv:1906.07773, 2019.
Mark Stamp. Introduction to machine learning with applications in information security. CRC Press, Taylor & Francis Group, Boca Raton, FL, 2018.
Yi Xiang Marcus Tan, Alfonso Iacovazzi, Ivan Homoliak, Yuval Elovici, and Alexander Binder. Adversarial attacks on remote user authentication using behavioural mouse dynamics. In 2019 International Joint Conference on Neural Networks, IJCNN, pages 1–10, 2019.
Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Jing Jiang, and Michael Blumenstein. Rethinking 1d-CNN for time series classification: A stronger baseline. arXiv preprint arXiv:2002.10061, 2020.
Romain Tavenard. tslearn documentation. https://tslearn.readthedocs.io/en/stable/, 2021.
Cong Wu, Kun He, Jing Chen, Ziming Zhao, and Ruiying Du. Liveness is not enough: Enhancing fingerprint authentication with behavioral biometrics to defeat puppet attacks. In 29th USENIX Security Symposium, USENIX Security 20, pages 2219–2236, 2020.
Jonathan Wu, Prakash Ishwar, and Janusz Konrad. Two-stream CNNs for gesture-based verification and identification: Learning user style. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 42–50, 2016.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Huang, E., Troia, F.D., Stamp, M. (2022). Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication. In: Stamp, M., Aaron Visaggio, C., Mercaldo, F., Di Troia, F. (eds) Artificial Intelligence for Cybersecurity. Advances in Information Security, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-030-97087-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-97087-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97086-4
Online ISBN: 978-3-030-97087-1
eBook Packages: Computer ScienceComputer Science (R0)