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2514 results in Pattern Recognition and Machine Learning

Page 86 of 126

  • 8 - Near-Optimal Evasion of Classifiers

  • from Part III - Exploratory Attacks on Machine Learning
  • Anthony D. Joseph, University of California, Berkeley, Blaine Nelson, Benjamin I. P. Rubinstein, University of Melbourne, J. D. Tygar, University of California, Berkeley
  • Book:
    Adversarial Machine Learning
    Published online:
    14 March 2019
    Print publication:
    21 February 2019, pp 199-238

  • Summary

    In this chapter, we explore a theoretical model for quantifying the difficulty of Exploratory attacks against a trained classifier. Unlike the previous work, since the classifier has already been trained, the adversary can no longer exploit vulnerabilities in the learning algorithm to mistrain the classifier as we demonstrated in the first part of this book. Instead, the adversary must exploit vulnerabilities that the classifier accidentally acquired from training on benign data (or at least data not controlled by the adversary in question). Most nontrivial classification tasks will lead to some form of vulnerability in the classifier. All known detection techniques are susceptible to blind spots (i.e., classes of miscreant activity that fail to be detected), but simply knowing that they exist is insufficient. The principal question is how difficult it is for an adversary to discover a blind spot that is most advantageous for the adversary. In this chapter, we explore a framework for quantifying how difficult it is for the adversary to search for this type of vulnerability in a classifier.

    At first, it may appear that the ultimate goal of these Exploratory attacks is to reverse engineer the learned parameters, internal state, or the entire boundary of a classifier to discover its blind spots. However, in this work, we adopt a more refined strategy; we demonstrate successful Exploratory attacks that only partially reverse engineer the classifier. Our techniques find blind spots using only a small number of queries and yield near-optimal strategies for the adversary. They discover data points that the classifier will classify as benign and that are close to the adversary's desired attack instance.

    While learning algorithms allow the detection algorithm to adapt over time, realworld constraints on the learning algorithm typically allow an adversary to programmatically find blind spots in the classifier. We consider how an adversary can systematically discover blind spots by querying the filter to find a low-cost (for some cost function) instance that evades the filter. Consider, for example, a spammer who wishes to minimally modify a spam message so it is not classified as spam (here cost is a measure of how much the spam must be modified). By observing the responses of the spam detector, the spammer can search for a modification while using few queries.

High-Dimensional Statistics
  • A Non-Asymptotic Viewpoint
  • Martin J. Wainwright
  • Published online:
    12 February 2019
    Print publication:
    21 February 2019
  • Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to large-scale data.

Algorithmic Aspects of Machine Learning
  • Ankur Moitra
  • Published online:
    28 September 2018
    Print publication:
    27 September 2018
  • This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems.

Data Mining and Analysis
  • Fundamental Concepts and Algorithms
  • Mohammed J. Zaki, Wagner Meira, Jr
  • Published online:
    28 May 2018
    Print publication:
    12 May 2014
  • The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike.

Predictive Statistics
  • Analysis and Inference beyond Models
  • Bertrand S. Clarke, Jennifer L. Clarke
  • Published online:
    27 April 2018
    Print publication:
    12 April 2018
  • All scientific disciplines prize predictive success. Conventional statistical analyses, however, treat prediction as secondary, instead focusing on modeling and hence estimation, testing, and detailed physical interpretation, tackling these tasks before the predictive adequacy of a model is established. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook. The book also considers prediction via contemporary 'black box' techniques and emerging data types and methodologies where conventional modeling is so difficult that good prediction is the main criterion available for evaluating the performance of a statistical method. Well-documented open-source R code in a Github repository allows readers to replicate examples and apply techniques to other investigations.

Page 86 of 126