Continuous Authentication of Mouse Dynamics Based on Decision Level Fusion

Gao, Lifang; Lian, Yangyang; Yang, Hu; Rui, Xiaobo; Yu, Zhuozhi; Chen, Wenwei; Liu, Wei; Zhang, Yefeng; Zhu, Yukun; Xu, Siya; Guo, Shaoyong; Cheng, Yanjin

doi:10.1109/iwcmc48107.2020.9148499

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Thus, one group's proneness to a certain error may not be as prevalent in the other groups. An example of this in the context of mouse dynamics has already been observed in [31] Combining the decisions from all of the classifiers then minimizes each other's errors and create a singular strong classifying method that is unlikely to overfit. With regards to mouse dynamics, RFs serve as a reliable middle ground between the stark contrasts of KNNs and SVMs.…”

Section: Random Forestmentioning

confidence: 97%

Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication

Siddiqui

Dave

Vanamala

et al. 2022

MAKE

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Static authentication methods, like passwords, grow increasingly weak with advancements in technology and attack strategies. Continuous authentication has been proposed as a solution, in which users who have gained access to an account are still monitored in order to continuously verify that the user is not an imposter who had access to the user credentials. Mouse dynamics is the behavior of a user’s mouse movements and is a biometric that has shown great promise for continuous authentication schemes. This article builds upon our previous published work by evaluating our dataset of 40 users using three machine learning and three deep learning algorithms. Two evaluation scenarios are considered: binary classifiers are used for user authentication, with the top performer being a 1-dimensional convolutional neural network (1D-CNN) with a peak average test accuracy of 85.73% across the top-10 users. Multi-class classification is also examined using an artificial neural network (ANN) which reaches an astounding peak accuracy of 92.48%, the highest accuracy we have seen for any classifier on this dataset.

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Section: Random Forestmentioning

confidence: 97%

Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication

Siddiqui

Dave

Vanamala

et al. 2022

MAKE

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“…The results show that decision level fusion effectively improves the classification performance of hyperspectral remote sensing images. Gao [13] proposed a decision-level fusion method of two classifiers to reduce the strong dependence on data during training. In this method, the error rate of the combination of two algorithms is lower than that of one algorithm.…”

Section: Data Fusionmentioning

confidence: 99%

MLfus: A real‐time forecasting architecture for low communication costs in electricity IoT based on ensemble learning

Li¹,

Zhang²,

Li³

et al. 2022

IET Communications

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With the application and popularity of Internet of Things (IoT) technology, real-time prediction of time series data has become the focus of electricity IoT data governance. At present, most of the time-series data prediction methods for the electricity IoT have the defect of being unable to process-related information between sequences. What's worse, the mainstream data fusion methods all have the problem of limited data dimension. This paper proposes a decision-level fusion architecture MLfus for multi-source time-series data generated under the distributed cloud edge structure of the electricity IoT. The model uses ensemble learning to make decisions and judgments on distributed time-series data and integrates multi-source data to make real-time predictions. MLfus solves the problem of significantly biased predictions from a single model and excels in handling complex nonlinear problems. What's more, MLfus can reduce data and additional training requirements substantially by using decision-level fusion. Experimental results show that MLfus has a clear advantage in the problem of real-time electricity price prediction, providing better accuracy while reducing the communication burden.

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A broad review on non-intrusive active user authentication in biometrics

Thomas¹,

Mathew

2021

J Ambient Intell Human Comput

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Authentication is the process of keeping the user's personal information as confidential in digital applications. Moreover, the user authentication process in the digital platform is employed to verify the own users by some authentication methods like biometrics, voice recognition, and so on. Traditionally, a one-time login based credential verification method was utilized for user authentication. Recently, several new approaches were proposed to enhance the user authentication framework but those approaches have been found inconsistent during the authentication execution process. Hence, the main motive of this review article is to analyze the advantage and disadvantages of authentication systems such as voice recognition, keystroke, and mouse dynamics. These authentication models are evaluated in a continuous non-user authentication environment and their results have been presented in way of tabular and graphical representation. Also, the common merits and demerits of the discussed authentication systems are broadly explained discussion section. Henceforth, this study will help the researchers to adopt the best suitable method at each stage to build an authentication framework for non-intrusive active authentication.

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Continuous Authentication of Mouse Dynamics Based on Decision Level Fusion

Cited by 5 publications

References 11 publications

Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication

Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication

MLfus: A real‐time forecasting architecture for low communication costs in electricity IoT based on ensemble learning

A broad review on non-intrusive active user authentication in biometrics

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