Adding Robustness to Support Vector Machines Against Adversarial Reverse Engineering
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Computational Bioscience Research Center (CBRC)
Structural and Functional Bioinformatics Group
Machine Intelligence & kNowledge Engineering Lab
Date
2014Permanent link to this record
http://hdl.handle.net/10754/565844
Metadata
Show full item recordAbstract
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:10.1145/2661829.2662047Conference/Event name
23rd ACM International Conference on Information and Knowledge Management, CIKM 2014ae974a485f413a2113503eed53cd6c53
10.1145/2661829.2662047