Computer Science ›› 2018, Vol. 45 ›› Issue (1): 34-38, 46.doi: 10.11896/j.issn.1002-137X.2018.01.005

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Ensemble Method Against Evasion Attack with Different Strength of Attack

LIU Xiao-qin, WANG Jie-ting, QIAN Yu-hua and WANG Xiao-yue   

  • Online:2018-01-15 Published:2018-11-13

Abstract: Driven by the illegal purpose,attackers often exploit the vulnerability of the classifier to make the malicious samples free of detection in adversarial learning.At present,adversarial learning has been widely used in computer network intrusion detection,spam filtering,biometrics identification and other fields.Many researchers only apply the exi-sting ensemble methods in adversarial learning,and prove that multiple classi-fiers are more robust than single classi-fier.However,priori information about the attacker has a great influence on the robustness of the classifier in adversariallearning.Based on this situation,by simulating different strength of attack in learning process and increasing the weight of the misclassified sample,the robustness of the multiple classifiers can be improved with maintaining the accuracy.The experimental results show that the ensemble algorithm against evasion attack with different strength of attack is more robust than Bagging.Finally,the convergence of the algorithm and the influence of parameter on the algorithm were analyzed.

Key words: Adversarial learning,Evasion attacks,Multiple classifier systems,Robustness

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