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

Published: 06 December 2021 Publication History

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.

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cover image ACM Other conferences
ACSAC '21: Proceedings of the 37th Annual Computer Security Applications Conference
December 2021
1077 pages
ISBN:9781450385794
DOI:10.1145/3485832
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 06 December 2021

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Author Tags

  1. Behavior Authentication Code
  2. Evasion Attack Detection
  3. Human-Machine Recognition
  4. Machine Learning
  5. Mouse Trajectory

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ACSAC '21

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Overall Acceptance Rate 104 of 497 submissions, 21%

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