Journal ArticleDOI
Multiple classifier systems for robust classifier design in adversarial environments
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TLDR
This paper focuses on a strategy recently proposed in the literature to improve the robustness of linear classifiers to adversarial data manipulation, and experimentally investigates whether it can be implemented using two well known techniques for the construction of multiple classifier systems, namely, bagging and the random subspace method.Abstract:
Pattern recognition systems are increasingly being used in adversarial environments like network intrusion detection, spam filtering and biometric authentication and verification systems, in which an adversary may adaptively manipulate data to make a classifier ineffective. Current theory and design methods of pattern recognition systems do not take into account the adversarial nature of such kind of applications. Their extension to adversarial settings is thus mandatory, to safeguard the security and reliability of pattern recognition systems in adversarial environments. In this paper we focus on a strategy recently proposed in the literature to improve the robustness of linear classifiers to adversarial data manipulation, and experimentally investigate whether it can be implemented using two well known techniques for the construction of multiple classifier systems, namely, bagging and the random subspace method. Our results provide some hints on the potential usefulness of classifier ensembles in adversarial classification tasks, which is different from the motivations suggested so far in the literature.read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Book ChapterDOI
Evasion attacks against machine learning at test time
Battista Biggio,Igino Corona,Davide Maiorca,Blaine Nelson,Nedim Srndic,Pavel Laskov,Giorgio Giacinto,Fabio Roli +7 more
TL;DR: This work presents a simple but effective gradient-based approach that can be exploited to systematically assess the security of several, widely-used classification algorithms against evasion attacks.
Journal ArticleDOI
Adversarial Examples: Attacks and Defenses for Deep Learning
TL;DR: In this paper, the authors review recent findings on adversarial examples for DNNs, summarize the methods for generating adversarial samples, and propose a taxonomy of these methods.
Book ChapterDOI
Evasion Attacks against Machine Learning at Test Time
Battista Biggio,Igino Corona,Davide Maiorca,Blaine Nelson,Nedim Srndic,Pavel Laskov,Giorgio Giacinto,Fabio Roli +7 more
TL;DR: In this paper, the authors present a simple but effective gradient-based approach that can be exploited to systematically assess the security of several, widely-used classification algorithms against evasion attacks.
Proceedings Article
Poisoning Attacks against Support Vector Machines
TL;DR: In this paper, the authors investigate a family of poisoning attacks against Support Vector Machines (SVM) and demonstrate that an intelligent adversary can predict the change of the SVM's decision function due to malicious input and use this ability to construct malicious data.
References
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Random Forests
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LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
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Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Journal ArticleDOI
Bagging predictors
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.