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An Efficient Man-Machine Recognition Method Based On Mouse Trajectory Feature De-redundancy
Behavioral authentication codes are widely used to resist abnormal network traffic. Mouse sliding behavior as an authentication method has the characteristics of less private information and easy data sampling. This paper analyses the attack mode of the machine sliding track data, extracts the physical quantity characteristics of the sliding path, uses the model score to select the features with good classification results, and further uses Pearson correlation coefficient to filter out the features with high correlation. This paper use XGBoost model as a classifier. In addition, an efficient replay attack detection method is proposed to deal with complex human behavior replay attacks. In this paper, two mouse sliding datasets are tested. The experimental results show that the proposed method achieves 99.09\% accuracy and 99.88\% recall rate, and can complete a man-machine identification in 2ms, with excellent detection time efficiency.