Beware of SMOMBIES: Verification of Users Based on Activities While Walking

Several research evaluated the user's style of walking for the verification of a claimed identity and showed high authentication accuracies in many settings. In this paper we present a system that successfully verifies a user's identity based on many real world smartphone placements and yet not regarded interactions while walking. Our contribution is the distinction of all considered activities into three distinct subsets and a specific one-class Support Vector Machine per subset. Using sensor data of 30 participants collected in a semi-supervised study approach, we prove that unsupervised verification is possible with very low false-acceptance and false-rejection rates. We furthermore show that these subsets can be distinguished with a high accuracy and demonstrate that this system can be deployed on off-the-shelf smartphones.

[1]  Florian Alt,et al.  Understanding Shoulder Surfing in the Wild: Stories from Users and Observers , 2017, CHI.

[2]  Yuji Watanabe,et al.  Influence of Holding Smart Phone for Acceleration-Based Gait Authentication , 2014, 2014 Fifth International Conference on Emerging Security Technologies.

[3]  Dawn Xiaodong Song,et al.  Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication , 2012, IEEE Transactions on Information Forensics and Security.

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  Rajesh Kumar,et al.  Continuous authentication using one-class classifiers and their fusion , 2017, 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA).

[6]  Agata Brajdic,et al.  Walk detection and step counting on unconstrained smartphones , 2013, UbiComp.

[7]  Zhao Wang,et al.  Modeling interactive sensor-behavior with smartphones for implicit and active user authentication , 2017, 2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA).

[8]  Jamario White,et al.  Distracted walking: Examining the extent to pedestrian safety problems , 2015 .

[9]  Chris Van Hoof,et al.  Self-calibration of walking speed estimations using smartphone sensors , 2014, 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS).

[10]  Ussain,et al.  Continuous and Transparent User Identity Verification for Secure Internet Services , 2015 .

[11]  Teddy Mantoro,et al.  Real-time activity recognition in mobile phones based on its accelerometer data , 2016, 2016 International Conference on Informatics and Computing (ICIC).

[12]  Jun Yang,et al.  SenGuard: Passive user identification on smartphones using multiple sensors , 2011, 2011 IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[13]  Matteo Gadaleta,et al.  IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks , 2016, Pattern Recognit..

[14]  Ling Chen,et al.  An Interpretable Orientation and Placement Invariant Approach for Smartphone Based Activity Recognition , 2015, 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom).

[15]  Faicel Chamroukhi,et al.  An Unsupervised Approach for Automatic Activity Recognition Based on Hidden Markov Model Regression , 2013, IEEE Transactions on Automation Science and Engineering.

[16]  Prasant Mohapatra,et al.  WearIA: Wearable device implicit authentication based on activity information , 2017, 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[17]  Frank Stajano,et al.  The Quest to Replace Passwords: A Framework for Comparative Evaluation of Web Authentication Schemes , 2012, 2012 IEEE Symposium on Security and Privacy.

[18]  Yuji Watanabe,et al.  Toward an Immunity-based Gait Recognition on Smart Phone: A Study of Feature Selection and Walking State Classification , 2016, KES.

[19]  Adam J. Aviv,et al.  Smudge Attacks on Smartphone Touch Screens , 2010, WOOT.

[20]  Ana M. Bernardos,et al.  Activity logging using lightweight classification techniques in mobile devices , 2012, Personal and Ubiquitous Computing.

[21]  Lama Nachman,et al.  Unobtrusive gait verification for mobile phones , 2014, SEMWEB.

[22]  Bruno Crispo,et al.  Mobile biometrics: Towards a comprehensive evaluation methodology , 2017, 2017 International Carnahan Conference on Security Technology (ICCST).

[23]  Konrad P. Kording,et al.  Journal of Neuroscience Methods , 2013 .

[24]  Wenyao Xu,et al.  EyeVeri: A secure and usable approach for smartphone user authentication , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[25]  Jie Yang,et al.  Smartphone based user verification leveraging gait recognition for mobile healthcare systems , 2013, 2013 IEEE International Conference on Sensing, Communications and Networking (SECON).

[26]  Ruby B. Lee,et al.  Implicit Sensor-based Authentication of Smartphone Users with Smartwatch , 2016, HASP 2016.