User Authentication Method Based on MKL for Keystroke and Mouse Behavioral Feature Fusion

Wang, Xiujuan; Zheng, Qianqian; Zheng, Kan; Wu, Tong

doi:10.1155/2020/9282380

Cited by 8 publications

(2 citation statements)

References 43 publications

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“…Also, the accuracy of discriminating identity trustworthiness by the interactor's input behavior on the keyboard (Kim et al, 2017) and tapping rhythm (Mhenni et al, 2019) exceeds 93%. Further, Wang et al (2020) achieved 98% accuracy through the combination of keyboard and mouse behavior methods. However, it is the practical implementation that the collection of these data requires the deployment of custom code in the website, which will reduce the fluency of the interaction between humans and website and affect the interaction experience.…”

Section: Prior Research On Discriminating the Trustworthiness Of Iden...mentioning

confidence: 99%

Developing virtual fingerprint: identifying trustworthy interaction behaviors of web users

Sangbong

et al. 2022

Preprint

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Trustworthy interaction refers to the interaction process between human and network information systems that is believable and credible. In recent years, network security events frequently happened caused by human-web interaction lacking trustworthiness, which brings about some security risks such as private information leakage, resource abuse, property or reputation loss, etc. It is a significant network security concern threatening people. The problem is typically approached from the viewpoint of identifying the trustworthiness of the credentials submitted by interactors. There are little few studies that have addressed it in terms of the trustworthiness of the interaction behavior by interactors. We propose that the interaction behavior pattern of interactors on the website is a virtual fingerprint (VF) to discriminate the trustworthiness of the identity based on relevant psychological and behavioral theories. Using 481,838 weblog data from a travel service website, we demonstrate the effectiveness of the proposed VF. First, the individual differences in interaction behavior of interactors on the website are characterized by numerical analysis and statistical inference. Then, 15 interaction behavior features are constructed from weblogs to create a VF, which can discriminate the trustworthiness of the identity by identifying the interaction behaviors of the interactors on the website. Of these, a machine learning algorithm (i.e., the eXtreme gradient boosting) is applied to build the identification model for interaction behaviors. The results show that the VF proposed in this study has better discrimination of trustworthiness compared to those approaches that use other kinds of behavior features. And, the eXtreme gradient boosting used performs more efficiently than the algorithm of Decision Tree and Random Forest. Specifically, the errors of discriminating both untrustworthy and trustworthy identities are as low as 0.19% and 3.30%, respectively. Also, the accuracy of identifying interaction behaviors is as good as 99.63%.

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Section: Prior Research On Discriminating the Trustworthiness Of Iden...mentioning

confidence: 99%

Developing virtual fingerprint: identifying trustworthy interaction behaviors of web users

Sangbong

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, for complex problems such as large sample sizes, irregular multidimensional data, and an uneven distribution, it is limiting to use a single kernel function for processing. In this regard, multiple kernel functions can be combined, and the optimal linear combination of multiple kernels can be used to replace the single kernel to obtain better model performance, that is, the so-called Multiple Kernel Learning method [32].…”

Section: Introduction To Mkl-svr Theorymentioning

confidence: 99%

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

Sun,

Dai,

et al. 2024

Electronics

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The sound insulation performance of an electric vehicle’s body system serves as a critical metric for evaluating the noise, vibration, and harshness (NVH) quality of the vehicle. The accurate and efficient prediction of sound insulation performance is foundational for undertaking noise reduction design and optimization. Current engineering practices predominantly rely on Computer-Aided Engineering (CAE) methodologies to address this challenge. However, inherent shortcomings such as low modeling efficiency and difficulty in ensuring prediction accuracy often characterize these approaches. In an effort to overcome these limitations, we propose a decomposition framework for predicting the sound insulation performance of the electric vehicle body system. This framework is established based on a comprehensive analysis of the noise transmission paths within the system. Subsequently, the support vector regression (SVR) method is introduced to construct a machine learning model specifically designed for predicting the sound insulation performance of the body system. This approach aims to mitigate the inherent weaknesses associated with the conventional CAE processes using a ‘data-driven’ paradigm. Furthermore, the Multiple Kernel Learning (MKL) method is used to enhance the processing efficacy of the SVR model. The proposed method is validated using practical application and testing on a specific electric vehicle. The results demonstrate commendable performance in terms of prediction accuracy and robustness. This research contributes to advancing the field by presenting a more effective and reliable approach to predicting the sound insulation performance of electric vehicle body systems, offering valuable insights for noise reduction strategies and optimization efforts in the automotive industry.

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Rule-Based Credit Card Fraud Detection Using User’s Keystroke Behavior

Kumar

Saxena

2022

Soft Computing: Theories and Applications

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User Authentication Method Based on MKL for Keystroke and Mouse Behavioral Feature Fusion

Cited by 8 publications

References 43 publications

Developing virtual fingerprint: identifying trustworthy interaction behaviors of web users

Developing virtual fingerprint: identifying trustworthy interaction behaviors of web users

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

Rule-Based Credit Card Fraud Detection Using User’s Keystroke Behavior

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