WearIA: Wearable device implicit authentication based on activity information

Zeng, Yunze; Pande, Amit; Zhu, Jindan; Mohapatra, Prasant

doi:10.1109/wowmom.2017.7974305

Cited by 28 publications

(30 citation statements)

References 19 publications

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“…However, wearables also raise new challenges, specifically in terms of security. Unauthorized access of a wearable can enable access to other sensitive IoT objects, which poses a significant risk [1]. Unauthorized users could also access data on the wearables, e.g., many applications and services provided by a wearable depend on sensor and user data stored on the device.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in recent years, biometric-based solutions have been proposed, since they provide opportunities for implicit authentication by removing direct user involvement or attention [1], [3]. A market intelligence firm has also predicted that annual biometric hardware and software revenue will grow at a compound annual growth rate (CAGR) of 22.9% from $2.4 billion in 2016 to $15.1 billion worldwide by 2025 [4], which further provides evidence that there is an opportunity to utilize biometric-based authentication.…”

Section: Introductionmentioning

confidence: 99%

“…However, biometric-based authentication also has challenges and shortcomings, specifically in terms of accuracy and usability. For example, behavioral biometric-based approaches such as gait or gesture analysis often fail to authenticate a user during sedentary periods [1]. Due to the limited computational capabilities and energy resources, most traditional user authentication approaches, e.g., using fingerprints, voice, breathing patterns, keystroke dynamics, head or arm movements, gait, electroencephalography (EEG), and electrocardiograms (ECG) are often not suitable for wearable devices.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Modal Biometric-Based Implicit Authentication of Wearable Device Users

Vhaduri

Poellabauer

2019

IEEE Trans.Inform.Forensic Secur.

115

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Modal Biometric-Based Implicit Authentication of Wearable Device Users

Vhaduri

Poellabauer

2019

IEEE Trans.Inform.Forensic Secur.

115

View full text Add to dashboard Cite

“…Another approach for implicit identification and authentication based on activity information, WearAI, Zeng et al [21], proposed a biometric model that utilizes accelerometer and gyroscope sensors from five body locations such as left wrist (Shimmer 6DoF IMU), right ankle (Shimmer 6DoF IMU), center right hip/torso (Samsung Galaxy S4 i9500), left thigh/front pocket (Samsung Galaxy Nexus i9250), right upper arm (Samsung Galaxy Nexus i9250)). They achieved 97% accuracy with less than 1% false-positive rate.…”

Section: Related Workmentioning

confidence: 99%

Can a Smartband be Used for Continuous Implicit Authentication in Real Life

et al. 2020

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The use of cloud services that process privacy-sensitive information such as digital banking, pervasive healthcare, smart home applications requires an implicit continuous authentication solution, which will make these systems less vulnerable to the spoofing attacks. Physiological signals can be used for continuous authentication due to their uniqueness. Ubiquitous wrist-worn wearable devices are equipped with photoplethysmogram sensors, which enable us to extract heart rate variability (HRV) features. In this study, we show that these devices can be used for continuous physiological authentication for enhancing the security of the cloud, edge services, and IoT devices. A system that is suitable for the smartband framework comes with new challenges such as relatively low signal quality and artifacts due to placement, which were not encountered in full lead electrocardiogram systems. After the artifact removal, cleaned physiological signals are fed to the machine learning algorithms. In order to train our machine learning models, we collected physiological data using off-the-shelf smartbands and smartwatches in a real-life event. By applying a minimum quality threshold, we achieved a 3.96% Equal Error Rate. Performance evaluation shows that HRV is a strong candidate for continuous unobtrusive implicit physiological authentication.

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“…The possibility of connecting ordinary objects and obtaining data from them in real time has the potential to increase and improve the decision-making concerning a number of objects and situations that could not previously be measured. This opens up a substantial new area of applications in smart industries, as well as in the domains of education, biomedicine, and healthcare [ 4 , 5 , 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Easy-to-Build Textile Pressure Sensor

Pizarro

Villavicencio

Yunge

et al. 2018

Sensors

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This article presents the design, construction, and evaluation of an easy-to-build textile pressure resistive sensor created from low-cost conventional anti-static sheets and conductive woven fabrics. The sensor can be built quickly using standard household tools, and its thinness makes it especially suitable for wearable applications. Five sensors constructed under such conditions were evaluated, presenting a stable and linear characteristic in the range 1 to 70 kPa. The linear response was modeled and fitted for each sensor individually for comparison purposes, confirming a low variability due to the simple manufacturing process. Besides, the recovery times of the sensors were measured for pressures in the linear range, observing, for example, an average time of 1 s between the moment in which a pressure of 8 kPa was no longer applied, and the resistance variation at the 90% of its nominal value. Finally, we evaluated the proposed sensor design on a classroom application consisting of a smart glove that measured the pressure applied by each finger. From the evaluated characteristics, we concluded that the proposed design is suitable for didactic, healthcare and lifestyle applications in which the sensing of pressure variations, e.g., for activity assessment, is more valuable than accurate pressure sensing.

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WearIA: Wearable device implicit authentication based on activity information

Cited by 28 publications

References 19 publications

Multi-Modal Biometric-Based Implicit Authentication of Wearable Device Users

Multi-Modal Biometric-Based Implicit Authentication of Wearable Device Users

Can a Smartband be Used for Continuous Implicit Authentication in Real Life

Easy-to-Build Textile Pressure Sensor

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