RiskCog: Unobtrusive Real-Time User Authentication on Mobile Devices in the Wild

Pan

et al. 2019

Applied Sciences

Self Cite

Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using the following main steps: computer simulation of PQD signals, signal decomposition, feature extraction, heuristic selection of feature selection, and classification. First, different types of PQD signals are generated by computer simulation. Second, variational mode decomposition (VMD) is used to decompose the signals into several instinct mode functions (IMFs). Third, the statistical features are calculated in the time series for each IMF. Next, a two-stage feature selection method is imported to eliminate the redundant features by utilizing permutation entropy and the Fisher score algorithm. Finally, the selected feature vectors are fed into a multiclass support vector machine (SVM) model to classify the PQDs. Several experimental investigations are performed to verify the performance and effectiveness of the proposed method in a noisy environment. Moreover, the results demonstrate that the start and end points of the PQD can be efficiently detected.

Section: Permutation Entropymentioning

confidence: 99%

Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection

Pan

et al. 2019

Applied Sciences

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“…The permutation entropy is utilized to recognize the health status of rotation machines [36]. By counting the ordinal patterns, the temporal information of the monitored object is recognized by the permutation entropy.…”

Section: Permutation Entropymentioning

confidence: 99%

Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy

et al. 2019

Energies

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Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations in the monitored features, which may lead to an inaccurate prediction and maintenance schedule. In this scenario, this article proposed a novel methodology to detect the multiple levels of faults of rolling bearings in variable operating conditions. First, signal decomposition was carried out by variational mode decomposition (VMD). Second, the statistical features were calculated and extracted in the time domain. Meanwhile, a permutation entropy analysis was conducted to estimate the complexity of the vibrational signal in the time series. Next, feature selection techniques were applied to achieve improved identification accuracy and reduce the computational burden. Finally, the ranked feature vectors were fed into machine learning algorithms for the classification of the bearing defect status. In particular, the proposed method was performed over a wide range of working regions to simulate the operational conditions of wind turbines. Comprehensive experimental investigations were employed to evaluate the performance and effectiveness of the proposed method.

“…Although these two commonly used methods (knowledge-based authentication and static biometric-based authentication) have been in full swing for decades, there are a series of deficiencies in usability, security, and effectiveness. Therefore, in recent years, using motion sensors such as acceleration sensors, gyro sensors, and gravity sensors as data sources, a method of authentication based on the user’s dynamic behavior, has been proposed by many researchers (e.g., gait authentication [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ], user’s usage behavior authentication [ 32 , 33 , 34 , 35 , 36 , 37 , 38 ], and daily life behavior authentication [ 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]). These methods have better user experience and stronger privacy protection capabilities, laying the foundation for the development of the third phase of mobile user authentication.…”

Section: Introductionmentioning

confidence: 99%

“…There is no need to consider the impact of noise, but once deployed into the real-world, the availability is poor. Existing work based on large-scale real data [ 32 ] only filters the data lying on flat surfaces, but in the actual environment, there are many data unrelated to authentication, such as sensor signal discontinuities, mutation, etc., so the de-noising ability is limited. Previous work [ 52 ] has envisaged the use of recursive mode decomposition methods, such as empirical mode decomposition (EMD) [ 53 ] to de-noise, which adopts an adaptive method—through recursive steps the same decomposition method successively separates different modal components in the target signal.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors

Weng

Guo-lang

et al. 2020

Sensors

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With the popularity of smartphones and the development of hardware, mobile devices are widely used by people. To ensure availability and security, how to protect private data in mobile devices without disturbing users has become a key issue. Mobile user authentication methods based on motion sensors have been proposed by many works, but the existing methods have a series of problems such as poor de-noising ability, insufficient availability, and low coverage of feature extraction. Based on the shortcomings of existing methods, this paper proposes a hybrid deep learning system for complex real-world mobile authentication. The system includes: (1) a variational mode decomposition (VMD) based de-noising method to enhance the singular value of sensors, such as discontinuities and mutations, and increase the extraction range of the feature; (2) semi-supervised collaborative training (Tri-Training) methods to effectively deal with mislabeling problems in complex real-world situations; and (3) a combined convolutional neural network (CNN) and support vector machine (SVM) model for effective hybrid feature extraction and training. The training results under large-scale, real-world data show that the proposed system can achieve 95.01% authentication accuracy, and the effect is better than the existing frontier methods.