An Efficient Man-Machine Recognition Method Based On Mouse Trajectory Feature De-redundancy

Abstract

Behavioral authentication codes are widely used to resist abnor- mal 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. Features importance scores are used to select the candidate features, and further Pearson correlation co- efficient is used to filter out the features with high correlation. This paper use XGBoost model as a classifier. In addition, an efficient evasion attack detection method is proposed to deal with complex human behavior evasion attacks. The experiment was carried out on two mouse sliding datasets. The experimental results show that the proposed method achieves 99.09% accuracy and 99.88% recall rate, and can complete the man-machine identification in 2ms.