Performance Evaluation of Mobile Sensor for Context Awareness User Authentication

  • Eniola Adewumi Sheffield Hallam University
  • Timibloudi Enamamu
  • Aliyu Dahiru Sheffield Hallam University
Keywords: Mobile Sensor, Authentication, Mobile Device, Accelerometer


With the increase of smart devices and their capacities, their use for different services have also increased. As much as this is an advantage, it has posed additional risks because of the confidential information stored on them. This has increased the need for additional security on these systems. Most of the methods used for user authentication pose certain drawbacks that are either easy to circumvent or cumbersome to use. As a result, multi-level means of authentication is needed to improve the security of mobile devices. Sensors are playing a vital role in the mobile ecosystem to enhance different services. These sensors can be leveraged upon as a solution for user authentication. This research analyzed and evaluated different mobile device sensors for continuous and transparent user authentication. The mobile data used includes gyroscope, accelerometer, linear accelerometer, proximity, gravity, and magnetometer sensors’ data. Using a Feedforward Neural network for data classification after extracting features from the different sensors available in the mobile device; the most effective was selected by evaluating performance of the different sensors. The best sensor, the accelerometer was further experimented on. The experiment showed that smartphone accelerometer sensor exhibits sufficient discriminability, stability, and reliability for active and continuous authentication, by achieving a performance of 6.55% for the best overall EER.



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S. Balakrishna, M. Thirumaran and K. V. Solanki, “A Framework for IoT Sensor Data Acquisition and Analysis,” EAI Endorsed Transactions on Internet of Things, vol. 4, no. 16, 2018.

P. Markert, D. V. Bailey, M. Golla, M. Dürmuth and A. J. AviG, “This pin can be easily guessed: Analyzing the security of smartphone unlock pins,” IEEE Symposium on Security and Privacy (SP), pp. 286-303, 2020.

N. Chakraborty and S. Mondal, “Color Pass: An intelligent user interface to resist shoulder surfing attack,” in Proceedings of the 2014 IEEE Students’ Technology Symposium, 2014.

C. Shen, Y. Li, Y. Chen, X. Guan and R. A. Maxion, “Performance analysis of multi-motion sensor behavior for active smartphone authentication,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 1, pp. 48-62, 2017.

M. Muaaz, “A Transparent and Continuous Biometric Authentication Framework for User-Friendly Secure Mobile Environments,” UbiComp, pp. 4-7, 2013.

C. Giuffrida, K. Majdanik, M. Conti and H. & Bos, “I sensed it was you: authenticating mobile users with sensor-enhanced keystroke dynamics,” Proceedings of the 11th Conference on Detection of Intrusions and Malware and Vulnerability Assessment (DIMVA), pp. 92-111, 2014.

C. Nickel, T. Wirtl and C. Busch, “Authentication of smartphone users based on the way they walk using k-nn algorithm.,” Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 16-20, 2012.

G. Savedna and M. Haryana, “Biometrics in Mobile Security,” International Journal of Mobile & Adhoc Network, vol. 1, no. 1, pp. 14-17, 2011.

W. Yang, S. Wang, J. Hu, G. Zheng and C. Valli, “A fingerprint and finger-vein based cancelable multi-biometric system,” Pattern Recognition, vol. 78, pp. 242-251, 2018.

I. Natgunanathan, A. Mehmood, Y. Xiang, G. Beliakov and J. Yearwood, “Protection of privacy in biometric data,” IEEE Access, vol. 4, pp. 880-892, 2016.

A. K. Trivedi, D. M. Thounaojam and S. Pal, “A robust and non-invertible fingerprint template for fingerprint matching system,” Forensic Science International, vol. 288, pp. 256-265, 2018.

R. Purkait, “External Ear: An analysis of its uniqueness,” Egyptian Journal of Forensic Sciences, vol. 6, no. 2, pp. 99-107, 2016.

C. Lin and A. Kumar, “Contactless and partial 3D fingerprint recognition using multi-view deep representation,” Pattern Recognition, vol. 83, pp. 314-327, 2018

D. Jagadiswarya and D. Saraswady, “Biometric Authentication using Fused Multimodal Biometric,” Procedia Computer Science, vol. 85, pp. 109-116, 2016.

M. Ehatisham-ul-Haq, M. A. Azam, J. Loo, K. Shuang, S. Islam, U. Naeem and Y. Amin, “Authentication of Smartphone Users Based on Activity Recognition and Mobile Sensing,” Sensors, vol. 17, no. 9, pp. 1-31, 2017.

W.-H. Lee and R. B. Lee, “Multi sensor authentication to improve smartphone security,” International Conference on Information Systems Security and Privacy (ICISSP), pp. 5-30, 2016.

L. Hernandez-Alvarez, J. M. de Fuentes and L. González-Manzano, “SmartCAMPP - Smartphone-based continuous authentication leveraging motion sensors with privacy preservation,” Pattern Recognition Letters, vol. 147, pp. 189-196, 2021.

J. M. Espín López, A. Huertas Celdrán, J. G. Marín-Blázquez, F. Esquembre and G. Martínez Pérez, “S3: An AI-Enabled User Continuous Authentication for Smartphones Based on Sensors, Statistics and Speaker Information,” Sensors, vol. 21, no. 11, p. 3765, 2021.

M. Gomez-Barrero, J. Galbally and J. Fierrez, “Efficient Software attack on multimodal biometric systems and its application to face and iris fussion,” Pattern Recognition letters, vol. 36, pp. 243-253, 2014.

M. A. Alqarni, S. H. Chauhdary, M. N. Malik, M. E. U. Haq and M. A. Azam, “Identifying smartphone users based on how they interact with their phones,” Human-centric Computing and Information Sciences, vol. 10, no. 7, 2020.

A. Buriro, “Behavioral Biometrics for Smartphone user authentication,” International Doctoral School in Information Engineering and Communication Technologies (ICT), Italy, 2017.

R. D. Newbold, Newbold’s Biometric Dictionary: For Military and Industry, Bloomington: AuthorHouse, 2008.

A. Laghari and Z. A. Memon, “Biometric authentication technique using smartphone sensor,” 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 381-384, 2016.

I. Papavasileiou, Z. Qiao, C. Zhang, W. Zhang, J. Bi and S. Han, “GaitCode: Gait-based continuous authentication using multimodal learning and wearable sensors,” Smart Health, vol. 19, pp. 1-18, 2021.

How to Cite
E. Adewumi, T. Enamamu, and A. Dahiru, “Performance Evaluation of Mobile Sensor for Context Awareness User Authentication”, LAJC, vol. 9, no. 2, pp. 52-65, Jul. 2022.
Research Articles for the Regular Issue