Securing Mobile Device-Based Machine Interactions with User Location Histories

Marcus, Philipp; Kessel, Moritz; Linnhoff‐Popien, Claudia

doi:10.1007/978-3-642-33392-7_10

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…Inasmuch, the process is performed in an implicit manner during regular smartphone usage. Several implicit identity sensing approaches have been proposed in the past that leverage the sensors on mobile diveices such as the accelerometer [9], [10], GPS [11], touchscreen [12]- [17] cite out paper, microphone [18], and fingerprint sensor [19]- [22]. However, unlike previous works, we do not directly leverage the sensor readings and perform user authentication based upon this.…”

Section: Related Workmentioning

confidence: 98%

See 1 more Smart Citation

Secure Session on Mobile: An Exploration on Combining Biometric, TrustZone, and User Behavior

Feng¹,

Desalvo²,

Xu³

et al. 2014

Proceedings of the 6th International Conference on Mobile Computing, Applications and Services

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With the rise of Internet connected mobile devices, applications have migrated from PCs to mobile computing platforms. An important aspect, payment processing, faces new security challenges from these developments. Inasmuch, these advancements demand efforts from researchers and industry to meet increasing security needs. Threats can ensue from data loss, theft from lost, stolen, or decommissioned devices, informationstealing malware, and password peeping. We propose a secure framework for sensitive session driven applications which combines biometric-based continuous and implicit tracking of user identities, and TrustZone. This framework is accomplished through monitoring fingerprint authentication logs as well as detecting events when the phone has left the user's hands, all while in TrustZone, a platform for secure computation and storage on mobile devices. This solution leverages multiple onboard sensors as well as the ARM architecture to accomplish these feats. We conducted two user-studies acquiring smartphone users' usage statistics to investigate security and usability needs of our identity-tracking solution. To monitor these subtle gestures in real-world uncontrolled environments, multi-session data collection has been conducted to iteratively improve system performance. The evaluation results have demonstrated the feasibility of this framework as a secure session-based payment system.

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Section: Related Workmentioning

confidence: 98%

“…However, neither system provides a concrete design of user authentications on TrustZone. Researchers from ETH Zrich suggested utilizing TrustZone with a password to solve secure enrollment problem [11], but password authentication would be useless if the phone is lost and the password has been stolen.…”

Section: Related Workmentioning

confidence: 99%

Secure Session on Mobile: An Exploration on Combining Biometric, TrustZone, and User Behavior

Feng¹,

Desalvo²,

Xu³

et al. 2014

Proceedings of the 6th International Conference on Mobile Computing, Applications and Services

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show abstract

“…With the increasing popularity of portable devices, several implicit identity sensing approaches have been proposed by leveraging the sensors that can be found in a mobile device, including accelerometer [13], [14], GPS [15], touchscreen [16]- [20], microphone [9], and fingerprint sensor [21], [22]. In our research, we were inspired by SpeakerSense [23], a speaker identification prototype that performs continuous background sensing and speaker identification with minimal power requirements.…”

Section: Mobile Identity Managementmentioning

confidence: 99%

USR: Enabling Identity Awareness and Usable App Access Control During Hand-free Mobile Interactions

Feng¹,

Gao²,

Boumber³

et al. 2014

Proceedings of the 6th International Conference on Mobile Computing, Applications and Services

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We propose a new approach for improving user experience and privacy protection during human-mobile speech interaction by considering factors, such as user identity, application privacy level, usable app access control, and application function class. To integrate these factors into speech recognition on the mobile device, we design and implement a unified speechspeaker recognizer (USR) framework, which will recognize both speech content and speaker identification, and respond accordingly. This USR framework consists of an application interface, a speaker recognition module, a speech recognition module, and an identity management module. A comparison study was conducted contrasting the benefits and limitations of USR framework to the established original speech recognition and app access control framework on mobile device, such as Google Voice, and AppLock. Our results show that, while USR framework intuitively improved mobile privacy by serving only the phone owner for specific customized applications, USR framework was also able to provide better user experience across a number of tasks.

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“…In recent years, several other implicit identification approaches have been proposed leveraging smartphone's sensor devices such as accelerometer [41], touch screen [48], GPS [42] and microphone [40]. T. Feng, et al [21] proposed to extract finger motion speed and acceleration of touch patterns as features.…”

Section: Related Workmentioning

confidence: 99%

Extended sammon projection and wavelet kernel extreme learning machine for gait-based legitimate user identification

Khan

Mazzara

Distefano

et al. 2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

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Smartphones have ubiquitously integrated into our home and work environments, however, the user normally relies on explicit but inefficient identification processes in a controlled environment. Therefore, when the device is stolen, the attacker can have access to the user's personal information and services against the stored password/s. As a result of this potential scenario, this work demonstrates the possibilities of legitimate user identification in a semicontrolled environment through the built-in smartphones motion dynamics captured by two different sensors. This is a two-fold process, sub-activity recognition based user identification using the wavelet kernel extreme learning machine (KELM) to first recognize the performed activity and then identifies whether the recognized activity is performed by the legitimate user or impostor. Prior to the identification; Extended Sammon Projection (ESP) method has been deployed to reduce the redundancy between the features. To validate the proposed system, we first collected data from 20 users walking with their device freely placed in one of their pants pockets (front right, front left, back right and back left pocket). Through extensive experimentation's using overall and averageone-subject-cross-validation, we demonstrate that together time and frequency domain features optimized by ESP to train the KELM is an effective system to identify the legitimate user or an impostor with 97 − 98% overall accuracy.

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Securing Mobile Device-Based Machine Interactions with User Location Histories

Cited by 7 publications

References 10 publications

Secure Session on Mobile: An Exploration on Combining Biometric, TrustZone, and User Behavior

Secure Session on Mobile: An Exploration on Combining Biometric, TrustZone, and User Behavior

USR: Enabling Identity Awareness and Usable App Access Control During Hand-free Mobile Interactions

Extended sammon projection and wavelet kernel extreme learning machine for gait-based legitimate user identification

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