数学理论与应用 ›› 2017, Vol. 37 ›› Issue (3-4): 43-50.

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基于神经网络的滑动式验证码人机识别研究

梁小林, 陈林萍   

  1. 长沙理工大学数学与统计学院
  • 出版日期:2017-12-30 发布日期:2020-09-21
  • 基金资助:
    湖南省教育厅重点课题(17A003)

Research of Human Machine Identification for Sliding Verification Code Based on Neural Network

Liang Xiaolin, Chen Linping   

  1. School of Mathematics and Statistics,Changsha University of Science and Technology
  • Online:2017-12-30 Published:2020-09-21

摘要: 在滑动式验证码完成滑动验证的过程中,正确区分出操作者是“机器”还是“个人”对于网络安全至关重要.本文利用人和机器完成验证所留下的滑动轨迹提取特征,运用机器学习中的神经网络算法和 MATLAB软件对其进行实证研究和分析,建立神经网络分类模型预测验证操作者的类别.结果表明,BP神经网络模型预测准确度很高,在一定程度上为网络安全提供了保障.

关键词: 验证码, BP神经网络, 分类, ROC曲线, 人机识别

Abstract:

In the process of sliding verification,it is very important to distinguish whether the drill author is “machine”or“personal”.In this paper,we use the neural network algorithm and MATLAB software to make an empirical study and analysis on the characteristics of the sliding trajectory extraction left by human and machine,and establish the classification model of the neural network to predict the operator.The results show  that the BP neural network model has high prediction accuracy and provides a guarantee for network security to some extent.

Key words: Verification code, BP neural network, Classification, ROC curve, Human machine identification