Defense Against Poisoning Attack via Evaluating Training Samples Using Multiple Spectral Clustering Aggregation Method
Wentao Zhao1, Pan Li1,*, Chengzhang Zhu1,2, Dan Liu1, Xiao Liu1
College of Computer, National University of Defense Technology, Changsha, 410073, China.
Faculty of Engineering and Information Technology, University of Technology Sydney, 2007, Australia.
The defense techniques for machine learning are critical yet challenging due to the number and type of attacks for widely applied machine learning algorithms are significantly increasing. Among these attacks, the poisoning attack, which disturbs machine learning algorithms by injecting poisoning samples, is an attack with the greatest threat. In this paper, we focus on analyzing the characteristics of positioning samples and propose a novel sample evaluation method to defend against the poisoning attack catering for the characteristics of poisoning samples. To capture the intrinsic data characteristics from heterogeneous aspects, we first evaluate training data by multiple criteria, each of which is reformulated from a spectral clustering. Then, we integrate the multiple evaluation scores generated by the multiple criteria through the proposed multiple spectral clustering aggregation (MSCA) method. Finally, we use the unified score as the indicator of poisoning attack samples. Experimental results on intrusion detection data sets show that MSCA significantly outperforms the K-means outlier detection in terms of data legality evaluation and poisoning attack detection.
Zhao, W., Li, P., Zhu, C., Liu, D., Liu, X. (2019). Defense against poisoning attack via evaluating training samples using multiple spectral clustering aggregation method. Computers, Materials & Continua, 59(3), 817-832. https://doi.org/10.32604/cmc.2019.05957
Vancouver Style
Zhao W, Li P, Zhu C, Liu D, Liu X. Defense against poisoning attack via evaluating training samples using multiple spectral clustering aggregation method. Comput Mater Contin. 2019;59(3):817-832 https://doi.org/10.32604/cmc.2019.05957
IEEE Style
W. Zhao, P. Li, C. Zhu, D. Liu, and X. Liu "Defense Against Poisoning Attack via Evaluating Training Samples Using Multiple Spectral Clustering Aggregation Method," Comput. Mater. Contin., vol. 59, no. 3, pp. 817-832. 2019. https://doi.org/10.32604/cmc.2019.05957
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