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dc.contributor.authorAlabdulmohsin, Ibrahim
dc.contributor.authorGao, Xin
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2015-08-11T13:43:00Z
dc.date.available2015-08-11T13:43:00Z
dc.date.issued2014
dc.identifier.citationAlabdulmohsin, 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
dc.identifier.doi10.1145/2661829.2662047
dc.identifier.urihttp://hdl.handle.net/10754/565844
dc.description.abstractMany 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.
dc.publisherAssociation for Computing Machinery (ACM)
dc.subjectAdversarial learning
dc.subjectLinear SVM
dc.subjectReverse engineering
dc.titleAdding Robustness to Support Vector Machines Against Adversarial Reverse Engineering
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.identifier.journalProceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14
dc.conference.date3 November 2014 through 7 November 2014
dc.conference.name23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
kaust.personGao, Xin
kaust.personZhang, Xiangliang
kaust.personAlabdulmohsin, Ibrahim


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