Evaluation of random forest classifier in security domain

Evaluation of random forest classifier in security domain There is an intrinsic adversarial nature in the security domain such as spam filtering and malware detection systems that attempt to mislead the detection system. This adversarial nature makes security applications different from the classical machine learning problems; for instance, an adversary (attacker) might change the distribution of test data and violate the data stationarity, a common assumption in machine learning techniques. Since machine learning methods are not inherently adversary-aware, a classifier designer should investigate the robustness of a learning system under attack. In this respect, recent studies have modeled the identified attacks against machine learning-based detection systems. Based on this, a classifier designer can evaluate the performance of a learning system leveraging the modeled attacks. Prior research explored a gradient-based approach in order to devise an attack against a classifier with differentiable discriminant function like SVM. However, there are several powerful classifiers with non-differentiable decision boundary such as Random Forest, which are commonly used in different security domain and applications. In this paper, we present a novel approach to model an attack against classifiers with non-differentiable decision boundary. In the experimentation, we first present an example that visually shows the effect of a successful attack on the MNIST handwritten digits classification task. Then we conduct experiments for two well-known applications in the security domain: spam filtering and malware detection in PDF files. The experimental results demonstrate that the proposed attack successfully evades Random Forest classifier and effectively degrades the classifier’s performance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Evaluation of random forest classifier in security domain

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools
ISSN
0924-669X
eISSN
1573-7497
D.O.I.
10.1007/s10489-017-0907-2
Publisher site
See Article on Publisher Site

Abstract

There is an intrinsic adversarial nature in the security domain such as spam filtering and malware detection systems that attempt to mislead the detection system. This adversarial nature makes security applications different from the classical machine learning problems; for instance, an adversary (attacker) might change the distribution of test data and violate the data stationarity, a common assumption in machine learning techniques. Since machine learning methods are not inherently adversary-aware, a classifier designer should investigate the robustness of a learning system under attack. In this respect, recent studies have modeled the identified attacks against machine learning-based detection systems. Based on this, a classifier designer can evaluate the performance of a learning system leveraging the modeled attacks. Prior research explored a gradient-based approach in order to devise an attack against a classifier with differentiable discriminant function like SVM. However, there are several powerful classifiers with non-differentiable decision boundary such as Random Forest, which are commonly used in different security domain and applications. In this paper, we present a novel approach to model an attack against classifiers with non-differentiable decision boundary. In the experimentation, we first present an example that visually shows the effect of a successful attack on the MNIST handwritten digits classification task. Then we conduct experiments for two well-known applications in the security domain: spam filtering and malware detection in PDF files. The experimental results demonstrate that the proposed attack successfully evades Random Forest classifier and effectively degrades the classifier’s performance.

Journal

Applied IntelligenceSpringer Journals

Published: Apr 12, 2017

References

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