67,021 research outputs found
Security Evaluation of Support Vector Machines in Adversarial Environments
Support Vector Machines (SVMs) are among the most popular classification
techniques adopted in security applications like malware detection, intrusion
detection, and spam filtering. However, if SVMs are to be incorporated in
real-world security systems, they must be able to cope with attack patterns
that can either mislead the learning algorithm (poisoning), evade detection
(evasion), or gain information about their internal parameters (privacy
breaches). The main contributions of this chapter are twofold. First, we
introduce a formal general framework for the empirical evaluation of the
security of machine-learning systems. Second, according to our framework, we
demonstrate the feasibility of evasion, poisoning and privacy attacks against
SVMs in real-world security problems. For each attack technique, we evaluate
its impact and discuss whether (and how) it can be countered through an
adversary-aware design of SVMs. Our experiments are easily reproducible thanks
to open-source code that we have made available, together with all the employed
datasets, on a public repository.Comment: 47 pages, 9 figures; chapter accepted into book 'Support Vector
Machine Applications
Statistical Classifier Design and Evaluation
This thesis is concerned with the design and evaluation of statistical classifiers. This problem has an optimal solution with a priori knowledge of the underlying probability distributions. Here, we examine the expected performance of parametric classifiers designed from a finite set of training samples and tested under various conditions. By investigating the statistical properties of the performance bias when tested on the true distributions, we have isolated the effects of the individual design components (i.e., the number of training samples, the dimensionality, and the parameters of the underlying distributions). These results have allowed us to establish a firm theoretical foundation for new design guidelines and to develop an empirical approach for estimating the asymptotic performance. Investigation of the statistical properties of the performance bias when tested on finite sample sets has allowed us to pinpoint the effects of individual design samples, the relationship between the sizes of the design and test sets, and the effects of a dependency between these sets. This, in turn, leads to a better understanding of how a single training set can be used most efficiently. In addition, we have developed a theoretical framework for the analysis and comparison of various performance evaluation procedures. Nonparametric and one-class classifiers are also considered. The reduced Parzen classifier, a nonparametric classifier which combines the error estimation capabilities of the Parzen density estimate with the computational feasibility of parametric classifiers, is presented. Also, the effect of the distance-space mapping in a one-class classifier is discussed through the approximation of the performance of a distance-ranking procedure
Reactive Planar Manipulation with Convex Hybrid MPC
This paper presents a reactive controller for planar manipulation tasks that
leverages machine learning to achieve real-time performance. The approach is
based on a Model Predictive Control (MPC) formulation, where the goal is to
find an optimal sequence of robot motions to achieve a desired object motion.
Due to the multiple contact modes associated with frictional interactions, the
resulting optimization program suffers from combinatorial complexity when
tasked with determining the optimal sequence of modes.
To overcome this difficulty, we formulate the search for the optimal mode
sequences offline, separately from the search for optimal control inputs
online. Using tools from machine learning, this leads to a convex hybrid MPC
program that can be solved in real-time. We validate our algorithm on a planar
manipulation experimental setup where results show that the convex hybrid MPC
formulation with learned modes achieves good closed-loop performance on a
trajectory tracking problem
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