Continuous User Authentication on Multiple Smart Devices

Wang, Yajie; Zhang, Xiaomei; Hu, Haomin

doi:10.3390/info14050274

Cited by 3 publications

(1 citation statement)

References 36 publications

(40 reference statements)

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“…Safeguarding information privacy is crucial since people tend to keep identifiable information on their phones, including medical, financial, and identification information that could be misused if accessed by others [3]. To better protect data confidentiality, biometric user authentication is an essential security measure to implement [4,5].…”

Section: Introductionmentioning

confidence: 99%

Identifying Smartphone Users Based on Activities in Daily Living Using Deep Neural Networks

Mekruksavanich,

Jitpattanakul

2024

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Smartphones have become ubiquitous, allowing people to perform various tasks anytime and anywhere. As technology continues to advance, smartphones can now sense and connect to networks, providing context-awareness for different applications. Many individuals store sensitive data on their devices like financial credentials and personal information due to the convenience and accessibility. However, losing control of this data poses risks if the phone gets lost or stolen. While passwords, PINs, and pattern locks are common security methods, they can still be compromised through exploits like smudging residue from touching the screen. This research explored leveraging smartphone sensors to authenticate users based on behavioral patterns when operating the device. The proposed technique uses a deep learning model called DeepResNeXt, a type of deep residual network, to accurately identify smartphone owners through sensor data efficiently. Publicly available smartphone datasets were used to train the suggested model and other state-of-the-art networks to conduct user recognition. Multiple experiments validated the effectiveness of this framework, surpassing previous benchmark models in this area with a top F1-score of 98.96%.

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Section: Introductionmentioning

confidence: 99%

Identifying Smartphone Users Based on Activities in Daily Living Using Deep Neural Networks

Mekruksavanich,

Jitpattanakul

2024

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An Intelligent Self-Validated Sensor System Using Neural Network Technologies and Fuzzy Logic Under Operating Implementation Conditions

Vladov,

Vysotska,

Sokurenko

et al. 2024

BDCC

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This article presents an intelligent self-validated sensor system developed for dynamic objects and based on the intelligent sensor concept, which ensures autonomous data collection and real-time analysis while adapting to changing conditions and compensating for errors. The research’s scientific merit is that an intelligent self-validated sensor for dynamic objects has been developed that integrates adaptive correction algorithms, fuzzy logic, and neural networks to improve the sensors’ accuracy and reliability under changing operating conditions. The proposed intelligent self-validated sensor system provides real-time error compensation, long-term stability, and effective fault diagnostics. Analytical equations are described, considering corrections related to influencing factors, temporal drift, and calibration characteristics, significantly enhancing measurement accuracy and reliability. The fuzzy logic application allows for refining the scaling coefficient that adjusts the relationship between the measured parameter and influencing factors, utilizing fuzzy inference algorithms. Additionally, monitoring and diagnostics implementation for sensor states through LSTM networks enable effective fault detection. Computational experiments on the TV3-117 engine demonstrated high data-restoring accuracy during forced interruptions, reaching 99.5%. A comparative analysis with alternative approaches confirmed the advantages of using LSTM (Long Short-Term Memory) neural networks in improving measurement quality.

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Enhanced Security Access Control Using Statistical-Based Legitimate or Counterfeit Identification System

Edrah,

Ouda

2024

Computers

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With our increasing reliance on technology, there is a growing demand for efficient and seamless access control systems. Smartphone-centric biometric methods offer a diverse range of potential solutions capable of verifying users and providing an additional layer of security to prevent unauthorized access. To ensure the security and accuracy of smartphone-centric biometric identification, it is crucial that the phone reliably identifies its legitimate owner. Once the legitimate holder has been successfully determined, the phone can effortlessly provide real-time identity verification for various applications. To achieve this, we introduce a novel smartphone-integrated detection and control system called Identification: Legitimate or Counterfeit (ILC), which utilizes gait cycle analysis. The ILC system employs the smartphone’s accelerometer sensor, along with advanced statistical methods, to detect the user’s gait pattern, enabling real-time identification of the smartphone owner. This approach relies on statistical analysis of measurements obtained from the accelerometer sensor, specifically, peaks extracted from the X-axis data. Subsequently, the derived feature’s probability distribution function (PDF) is computed and compared to the known user’s PDF. The calculated probability verifies the similarity between the distributions, and a decision is made with 92.18% accuracy based on a predetermined verification threshold.

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Continuous User Authentication on Multiple Smart Devices

Cited by 3 publications

References 36 publications

Identifying Smartphone Users Based on Activities in Daily Living Using Deep Neural Networks

Identifying Smartphone Users Based on Activities in Daily Living Using Deep Neural Networks

An Intelligent Self-Validated Sensor System Using Neural Network Technologies and Fuzzy Logic Under Operating Implementation Conditions

Enhanced Security Access Control Using Statistical-Based Legitimate or Counterfeit Identification System

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