Article: Improve the robustness of data mining algorithm against adversarial evasion attack Journal: International Journal of Innovative Computing and Applications (IJICA) 2018 Vol.9 No.3 pp.142 - 149 Abstract: Conventional data mining theories developed for general-purpose applications commonly focus on the reducing the bias and variance on the ideal i.i.d. datasets, but neglecting its potential failure on maliciously generated data points by observing the system's behaviours. Therefore, dealing with these adversarial samples is an essential part of a security system to handle the data that are intentionally made to deceive the system. Due to this concern, this paper proposes a novel approach that introduces uncertainty to the model behaviour, in order to obfuscate the decision process of the attacking strategy and improve the robustness of security system against attacks that try to evade the detection. Our approach addresses three problems. First, we build a pool of mining models to improve robustness of a variety of mining algorithms, similar to ensemble learning but focusing on the optimisation the trade-off between off-line accuracy and robustness. Second, we randomly select a subset of models at run time (when the model is used for detection) to further boost the robustness. Third, we propose a theoretical framework that bounds the minimal number of features an attacker needs to modify given a set of selected models. Inderscience Publishers - linking academia, business and industry through research

Title: Improve the robustness of data mining algorithm against adversarial evasion attack

Authors: Ning Cao; Yingying Wang; Guofu Li; Yuyan Shen; Junshe Wang; Hongbin Zhang

Addresses: College of Information Engineering, Qingdao Binhai University, Qingdao, China ' College of Information Engineering, Qingdao Binhai University, Qingdao, China ' College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai, China ' College of Information Engineering, Qingdao Binhai University, Qingdao, China ' School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China ' School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China

Abstract: Conventional data mining theories developed for general-purpose applications commonly focus on the reducing the bias and variance on the ideal i.i.d. datasets, but neglecting its potential failure on maliciously generated data points by observing the system's behaviours. Therefore, dealing with these adversarial samples is an essential part of a security system to handle the data that are intentionally made to deceive the system. Due to this concern, this paper proposes a novel approach that introduces uncertainty to the model behaviour, in order to obfuscate the decision process of the attacking strategy and improve the robustness of security system against attacks that try to evade the detection. Our approach addresses three problems. First, we build a pool of mining models to improve robustness of a variety of mining algorithms, similar to ensemble learning but focusing on the optimisation the trade-off between off-line accuracy and robustness. Second, we randomly select a subset of models at run time (when the model is used for detection) to further boost the robustness. Third, we propose a theoretical framework that bounds the minimal number of features an attacker needs to modify given a set of selected models.

Keywords: data mining; robustness; security.

DOI: 10.1504/IJICA.2018.093732

International Journal of Innovative Computing and Applications, 2018 Vol.9 No.3, pp.142 - 149

Received: 15 Sep 2017
Accepted: 06 Dec 2017
Published online: 02 Aug 2018 *

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Title: Improve the robustness of data mining algorithm against adversarial evasion attack

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