最优加权对数变换转换HMOG特征的自动智能手机认证

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引用次数: 0

摘要

本文拟通过特征提取、加权对数变换和分类三个主要阶段来开发基于行为的智能手机自动认证模型。首先,从智能手机用户的触摸/手势相关数据中,借助抓握阻力和抓握稳定性提取手部运动、方向和抓握(HMOG)特征。通过对HMOG归一化,将提取的特征映射到特定的范围内。在加权对数变换阶段,将这些归一化的数据与权重相乘,然后进行对数变换。作为一种新颖的方法,对数和权重选择的决策过程是基于改进的优化算法,即基于阈值的修正鲸鱼优化算法(MT-WOA)。最后将特征向量输入到DBN中进行授权用户识别。最后,根据各种相关性能指标,在MT-WOA+DBN和现有模型之间进行基于性能的评估。
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Optimal Weighted Logarithmic Transformation Converted HMOG Features for Automatic Smart Phone Authentication
This paper intends to develop an automatic behavior based smart phone authentication model by three major phases: Feature extraction Weighted logarithmic transformation and Classification. Initially, from the data related to the touches/gesture of the Smartphone user, Hand Movement, Orientation, and Grasp (HMOG) features are extracted with the aid of grasp resistance and grasp stability. These extracted features are mapped within the particular range by normalizing HMOG. These normalized data are multiplied with the weights followed by logarithmic transformation in the weighted logarithmic transformation phase. As a novelty, the decision making process related to the logarithmic and weight selection is based on the improved optimization algorithm, so called as Modified Threshold-based Whale Optimization Algorithm (MT-WOA). The final feature vectors are fed to DBN for recognizing the authorized users. Finally, a performance based evaluation is performed between the MT-WOA+DBN and the existing models like in terms of various relevant performance measures.
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CiteScore
1.40
自引率
16.70%
发文量
23
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