WearIA: Wearable device implicit authentication based on activity information

Privacy and authenticity of data pushed by or into wearable devices are of important concerns. Wearable devices equipped with various sensors can capture user's activity in fine-grained level. In this work, we investigate the possibility of using user's activity information to develop an implicit authentication approach for wearable devices. We design and implement a framework that does continuous and implicit authentication based on ambulatory activities performed by the user. The system is validated using data collected from 30 participants with wearable devices worn across various regions of the body. The evaluation results show that the proposed approach can achieve as high as 97% accuracy rate with less than 1% false positive rate to authenticate a user using a single wearable device. And the accuracy rate can go up to 99.6% when we use the fusion of multiple wearable devices.

[1]  Shanhe Yi,et al.  GlassGesture: Exploring head gesture interface of smart glasses , 2016, INFOCOM.

[2]  Daniel Vogel,et al.  Targeted Mimicry Attacks on Touch Input Based Implicit Authentication Schemes , 2016, MobiSys.

[3]  Urs Hengartner,et al.  A Comparative Evaluation of Implicit Authentication Schemes , 2014, RAID.

[4]  Prasant Mohapatra,et al.  Energy expenditure estimation with smartphone body sensors , 2013 .

[5]  Shuangquan Wang,et al.  Continuous Authentication With Touch Behavioral Biometrics and Voice on Wearable Glasses , 2017, IEEE Transactions on Human-Machine Systems.

[6]  David Kotz,et al.  ZEBRA: Zero-Effort Bilateral Recurring Authentication , 2014, IEEE Symposium on Security and Privacy.

[7]  Shanshan Chen,et al.  Unsupervised activity clustering to estimate energy expenditure with a single body sensor , 2013, 2013 IEEE International Conference on Body Sensor Networks.

[8]  Parth H. Pathak,et al.  WiWho: WiFi-Based Person Identification in Smart Spaces , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[9]  Urs Hengartner,et al.  Itus: an implicit authentication framework for android , 2014, MobiCom.

[10]  Ashwin Ashok,et al.  Whose move is it anyway? Authenticating smart wearable devices using unique head movement patterns , 2016, 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[11]  Emmanuel,et al.  Using machine learning for real-time activity recognition and estimation of energy expenditure , 2008 .

[12]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[13]  Mohammad Omar Derawi,et al.  Accelerometer-Based Gait Analysis , A survey , 2010 .

[14]  Heinrich Hußmann,et al.  Touch me once and i know it's you!: implicit authentication based on touch screen patterns , 2012, CHI.

[15]  Jiska Snoeck-Stroband.  Internet-Based Self-Monitoring of Physical Activity (PA) in COPD Patients: a Study of the Usability of Activity Monitors and Patient’s Preferences , 2013 .

[16]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[17]  Anirban Mahanti,et al.  Gesture-Based Continuous Authentication for Wearable Devices: The Smart Glasses Use Case , 2016, ACNS.

[18]  Soumik Mondal,et al.  Swipe gesture based Continuous Authentication for mobile devices , 2015, 2015 International Conference on Biometrics (ICB).

[19]  Julien Penders,et al.  Energy expenditure estimation using wearable sensors: a new methodology for activity-specific models , 2012, Wireless Health.

[20]  Manuel Noguera,et al.  Energy Expenditure Analysis: A Comparative Research of Based on Mobile Accelerometers , 2014, IWAAL.

[21]  Xi Chen,et al.  Implicit User Re-authentication for Mobile Devices , 2009, UIC.

[22]  Amy Loutfi,et al.  Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges , 2013, Sensors.

[23]  Tao Feng,et al.  TIPS: context-aware implicit user identification using touch screen in uncontrolled environments , 2014, HotMobile.

[24]  Markus Jakobsson,et al.  Implicit Authentication through Learning User Behavior , 2010, ISC.

[25]  Walid Saad,et al.  On the authentication of devices in the Internet of things , 2016, 2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).