Adding Robustness to Support Vector Machines Against Adversarial Reverse Engineering
Citations Over TimeTop 16% of 2014 papers
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.
Related Papers
- → Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives(2023)12 cited
- → On The Detection Of Adversarial Attacks Through Reliable AI(2022)2 cited
- ADVANCEMENT OF ATTACK AND DEFENSE TECHNIQUES IN ADVERSARIAL MACHINELEARNING(2020)
- → An Optimal Control View of Adversarial Machine Learning(2018)9 cited
- → Analyzing the Impact of Adversarial Examples on Explainable Machine Learning(2023)