Abstract
Our research aims at classifying individuals based on their unique interactions on the touchscreen-based smartphones. In this research, we use ‘TouchAnalytics’ dataset, which include 41 subjects and 30 different behavioral features. Furthermore, we derived new features from the raw data to improve the overall authentication performance. Previous research has already been done on the TouchAnalytics dataset with the state-of-the-art classifiers, including Support Vector Machine (SVM) and k-nearest neighbor (kNN) and achieved equal error rates (EERs) between 0% to 4%. In this paper, we propose a Deep Neural Net (DNN) architecture to classify the individuals correctly. When we combine the new features with the existing ones, SVM and k-NN achieved the classification accuracies of 94.7% and 94.6%, respectively. This research explored seven other classifiers and out of them, decision tree and our proposed DNN classifiers resulted in the highest accuracies with 100%. The others included: Logistic Regression (LR), Linear Discriminant Analysis (LDA), Gaussian Naive Bayes (NB), Neural Network, and VGGNet with the accuracy scores of 94.7%, 95.9%, 31.9%, 88.8%, and 96.1%, respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cucu, P., Dascalescu, A.: Biometric authentication overview, advantages & disadvantages: how popular biometric methods work, and how to hack them (2017). https://heimdalsecurity.com/blog/biometric-authentication/
Frank, M., Biedert, R., Ma, E., Martinovic, I., Song, D.: Touchalytics: on the applicability of touchscreen input as a behavioral biometric for continuous authentication. IEEE Trans. Inf. Forensics Secur. 8(1), 136–148 (2013)
Zhenqiang Gong, N., Payer, M., Moazzezi, R., Frank, M.: Forgery-resistant touch-based authentication on mobile devices. In: ASIACCS 2016: 11th ACM Asia Conference on Computer and Communications Security, pp. 499–510. ACM (2016). (Acc. rate 20.9%) (2015)
Masood, R., Zi Hao Zhao, B., Asghar, H., Kaafar, M.: Touch and you’re trapp(ck)ed: quantifying the uniqueness of touch gestures for tracking. In: Proceedings on Privacy Enhancing Technologies, vol. 2018, issue 2, pp. 122–142 (2018)
Sae-Bae, N., Memon, N., Isbister, K., Ahmed, K.: Multitouch gesture-based authentication. IEEE Trans. Inf. Forensics Secur. 9(4), 568–582 (2014)
Meng, Y., Wong, D.S., Schlegel, R., Kwok, L.-f.: Touch gestures based biometric authentication scheme for touchscreen mobile phones. In: Kutyłowski, M., Yung, M. (eds.) Inscrypt 2012. LNCS, vol. 7763, pp. 331–350. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38519-3_21
Lee, Y., et al.: Touch based active user authentication using deep belief networks and random forests. In: The 6th IEEE International Conference on Information Communication and Management (ICICM 2016) (2016)
Shen, C., Zhang, Y., Guan, X., Maxion, R.A.: Performance analysis of touch-interaction behavior for active smartphone authentication. IEEE Trans. Inf. Forensics Secur. 11, 498–513 (2016)
Bo, C., Zhang, L., Li, X., Huang, Q., Wang, Y.: SilentSense: silent user identification via touch and movement behavioral biometrics. arXiv:1309.0073v1 [cs.CR], 31 August 2013
Maiorana, E., Campisi, P., González-Carballo, N., Neri, A.: Keystroke dynamics authentication for mobile phones. In: SAC 2011, TaiChung, Taiwan, 21–25 March 2011
Antala, M., Zsolt Szabo, L.: Biometric authentication based on touchscreen swipe patterns. In: 9th International Conference Interdisciplinarity in Engineering, INTER-ENG 2015, Tirgu-Mures, Romania, 8–9 October 2015
Knight, S., Littleton, K.: Discourse-centric learning analytics: mapping the terrain. J. Learn. Anal. 2(1), 185–209 (2015)
Shoukry, L., Göbel, S., Steinmetz, R.: Towards mobile multimodal learning analytics. In: Learning Analytics for and in Serious Games - Workshop, EC-TEL, p. 16 (2014)
Spiess, J., T’Joens, Y., Dragnea, R., Spencer, P., Philippart, L.: Using big data to improve customer experience and business performance. Bell Labs Techn. J. 18(4), 3–17 (2014). © 2014 Alcatel-Lucent. Published by Wiley Periodicals, Inc. Published online in Wiley Online Library (wileyonlinelibrary.com) (2014)
Idreosy, S., Liarou, E.: dbTouch: analytics at your fingertips. In: 6th Biennial Conference on Innovative Data Systems Research (CIDR) (2013)
Sitová, Z., et al.: HMOG: new behavioral biometric features for continuous authentication of smartphone users. IEEE Trans. Inf. Forensics Secur. 11(5), 877–892 (2016)
Brownlee, J.: Machine learning mastery with Python: understand your data, create accurate models and work projects end-to-end. Machine Learning Mastery (2017)
McCall, J.: Genetic algorithms for modelling and optimisation. J. Comput. Appl. Math. 184(1), 205–222 (2005)
Gad, A.: Introduction to Optimization with Genetic Algorithm. KDnuggets, March 2018. https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
Chatterjee, P., Roy, K.: Anti-spoofing approach using deep convolutional neural network. In: Mouhoub, M., Sadaoui, S., AM, O., Ali, M. (eds.) IEA/AIE 2018. LNCS (LNAI), vol. 10868, pp. 745–750. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92058-0_72
Acknowledgements
This research is based upon work supported by the Science & Technology Center: Bio/Computational Evolution in Action Consortium (BEACON) and the Army Research Office (Contract No. W911NF-15-1-0524).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Montgomery, M., Chatterjee, P., Jenkins, J., Roy, K. (2019). Touch Analysis: An Empirical Evaluation of Machine Learning Classification Algorithms on Touch Data. In: Wang, G., Feng, J., Bhuiyan, M., Lu, R. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2019. Lecture Notes in Computer Science(), vol 11611. Springer, Cham. https://doi.org/10.1007/978-3-030-24907-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-24907-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24906-9
Online ISBN: 978-3-030-24907-6
eBook Packages: Computer ScienceComputer Science (R0)