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    Adding Robustness to Support Vector Machines Against Adversarial Reverse Engineering

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    Type
    Conference Paper
    Authors
    Alabdulmohsin, Ibrahim cc
    Gao, Xin cc
    Zhang, Xiangliang cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Computational Bioscience Research Center (CBRC)
    Structural and Functional Bioinformatics Group
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2014
    Permanent link to this record
    http://hdl.handle.net/10754/565844
    
    Metadata
    Show full item record
    Abstract
    Many classification algorithms have been successfully deployed in security-sensitive applications including spam filters and intrusion detection systems. Under such adversarial environments, adversaries can generate exploratory attacks against the defender such as evasion and reverse engineering. In this paper, we discuss why reverse engineering attacks can be carried out quite efficiently against fixed classifiers, and investigate the use of randomization as a suitable strategy for mitigating their risk. In particular, we derive a semidefinite programming (SDP) formulation for learning a distribution of classifiers subject to the constraint that any single classifier picked at random from such distribution provides reliable predictions with a high probability. We analyze the tradeoff between variance of the distribution and its predictive accuracy, and establish that one can almost always incorporate randomization with large variance without incurring a loss in accuracy. In other words, the conventional approach of using a fixed classifier in adversarial environments is generally Pareto suboptimal. Finally, we validate such conclusions on both synthetic and real-world classification problems. Copyright 2014 ACM.
    Citation
    Alabdulmohsin, I. M., Gao, X., & Zhang, X. (2014). Adding Robustness to Support Vector Machines Against Adversarial Reverse Engineering. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM ’14. doi:10.1145/2661829.2662047
    Publisher
    Association for Computing Machinery (ACM)
    Journal
    Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14
    Conference/Event name
    23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
    DOI
    10.1145/2661829.2662047
    ae974a485f413a2113503eed53cd6c53
    10.1145/2661829.2662047
    Scopus Count
    Collections
    Conference Papers; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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