Robust Machine Learning
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Robust Machine Learning Algorithms And Systems For Detection And Mitigation Of Adversarial Attacks And Anomalies
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Author : National Academies of Sciences, Engineering, and Medicine
language : en
Publisher: National Academies Press
Release Date : 2019-08-22
Robust Machine Learning Algorithms And Systems For Detection And Mitigation Of Adversarial Attacks And Anomalies written by National Academies of Sciences, Engineering, and Medicine and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-22 with Computers categories.
The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11â€"12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.
Robust Machine Learning
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Author : Rachid Guerraoui
language : en
Publisher: Springer
Release Date : 2024-05-03
Robust Machine Learning written by Rachid Guerraoui and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-03 with Computers categories.
Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust machine learning algorithms. Studying the robustness of machine learning algorithms is a necessity given the ubiquity of these algorithms in both the private and public sectors. Accordingly, over the past few years, we have witnessed a rapid growth in the number of articles published on the robustness of distributed machine learning algorithms. We believe it is time to provide a clear foundation to this emerging and dynamic field. By gathering the existing knowledge and democratizing the concept of robustness, the book provides the basis for a new generation of reliable and safe machine learning schemes. In addition to introducing the problem of robustness in modern machine learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. Moreover, the book provides a foundation for future research in this area.
Adversarial Robustness For Machine Learning
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Author : Pin-Yu Chen
language : en
Publisher: Academic Press
Release Date : 2022-08-20
Adversarial Robustness For Machine Learning written by Pin-Yu Chen and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-20 with Computers categories.
Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and verification. Sections cover adversarial attack, verification and defense, mainly focusing on image classification applications which are the standard benchmark considered in the adversarial robustness community. Other sections discuss adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. For researchers, this book provides a thorough literature review that summarizes latest progress in the area, which can be a good reference for conducting future research. In addition, the book can also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning. While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems. - Summarizes the whole field of adversarial robustness for Machine learning models - Provides a clearly explained, self-contained reference - Introduces formulations, algorithms and intuitions - Includes applications based on adversarial robustness
Towards Robust Machine Learning
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Author : Ori Press
language : en
Publisher:
Release Date : 2025
Towards Robust Machine Learning written by Ori Press and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with categories.
Fundamentals Of Robust Machine Learning
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Author : Resve A. Saleh
language : en
Publisher: John Wiley & Sons
Release Date : 2025-04-14
Fundamentals Of Robust Machine Learning written by Resve A. Saleh and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-14 with Computers categories.
An essential guide for tackling outliers and anomalies in machine learning and data science. In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few. Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models. Fundamentals of Robust Machine Learning readers will also find: A blend of robust statistics and machine learning principles Detailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detection Python code with immediate application to data science problems Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers.
Robust Machine Learning Models And Their Applications
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Author : Hongge Chen (Ph. D.)
language : en
Publisher:
Release Date : 2021
Robust Machine Learning Models And Their Applications written by Hongge Chen (Ph. D.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Recent studies have demonstrated that machine learning models are vulnerable to adversarial perturbations – a small and human-imperceptible input perturbation can easily change the model output completely. This has created serious security threats to many real applications, so it becomes important to formally verify the robustness of machine learning models. This thesis studies the robustness of deep neural networks as well as tree-based models, and considers the applications of robust machine learning models in deep reinforcement learning. We first develop a novel algorithm to learn robust trees. Our method aims to optimize the performance under the worst case perturbation of input features, which leads to a max-min saddle point problem when splitting nodes in trees. We propose efficient tree building algorithms by approximating the inner minimizer in this saddle point problem, and present efficient implementations for classical information gain based trees as well as state-of-the-art tree boosting models such as XGBoost. Experiments show that our method improve the model robustness significantly. We also propose an efficient method to verify the robustness of tree ensembles. We cast the tree ensembles verification problem as a max-clique problem on a multipartite graph. We develop an efficient multi-level verification algorithm that can give tight lower bounds on robustness of decision tree ensembles, while allowing iterative improvement and termination at any-time. On random forest or gradient boosted decision trees models trained on various datasets, our algorithm is up to hundreds of times faster than the previous approach that requires solving a mixed integer linear programming, and is able to give tight robustness verification bounds on large ensembles with hundreds of deep trees. For neural networks, we contribute a number of empirical studies on the practicality and the hardness of adversarial training. We show that even with adversarial defense, a model’s robustness on a test example has a strong correlation with the distance between that example and the manifold of training data embedded by the network. Test examples that are relatively far away from this manifold are more likely to be vulnerable to adversarial attacks. Consequentially, we demonstrate that an adversarial training based defense is vulnerable to a new class of attacks, the “blind-spot attack,” where the input examples reside in low density regions (“blind-spots”) of the empirical distribution of training data but are still on the valid ground-truth data manifold. Finally, we apply neural network robust training methods to deep reinforcement learning (DRL) to train agents that are robust against perturbations on state observations. We propose the state-adversarial Markov decision process (SA-MDP) to study the fundamental properties of this problem, and propose a theoretically principled regularization which can be applied to different DRL algorithms, including deep Q networks (DQN) and proximal policy optimization (PPO). We significantly improve the robustness of agents under strong white box adversarial attacks, including new attacks of our own.
Machine Learning Algorithms
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Author : Fuwei Li
language : en
Publisher: Springer Nature
Release Date : 2022-11-14
Machine Learning Algorithms written by Fuwei Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-14 with Computers categories.
This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing algorithms against adversarial attacks. Since data quality is crucial in signal processing, the adversary that can poison the data will be a significant threat to signal processing. Therefore, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks. The authors in this book mainly examine the adversarial robustness of three commonly used machine learning algorithms in signal processing respectively: linear regression, LASSO-based feature selection, and principal component analysis (PCA). As to linear regression, the authors derive the optimal poisoning data sample and the optimal feature modifications, and also demonstrate the effectiveness of the attack against a wireless distributed learning system. The authors further extend the linear regression to LASSO-based feature selection and study the best strategy to mislead the learning system to select the wrong features. The authors find the optimal attack strategy by solving a bi-level optimization problem and also illustrate how this attack influences array signal processing and weather data analysis. In the end, the authors consider the adversarial robustness of the subspace learning problem. The authors examine the optimal modification strategy under the energy constraints to delude the PCA-based subspace learning algorithm. This book targets researchers working in machine learning, electronic information, and information theory as well as advanced-level students studying these subjects. R&D engineers who are working in machine learning, adversarial machine learning, robust machine learning, and technical consultants working on the security and robustness of machine learning are likely to purchase this book as a reference guide.
Robust Machine Learning In Adversarial Setting With Provable Guarantee
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Author : Yizhen Wang
language : en
Publisher:
Release Date : 2020
Robust Machine Learning In Adversarial Setting With Provable Guarantee written by Yizhen Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Over the last decade, machine learning systems have achieved state-of-the-art performance in many fields, and are now used in increasing number of applications. However, recent research work has revealed multiple attacks to machine learning systems that significantly reduce the performance by manipulating the training or test data. As machine learning is increasingly involved in high-stake decision making processes, the robustness of machine learning systems in adversarial environment becomes a major concern. This dissertation attempts to build machine learning systems robust to such adversarial manipulation with the emphasis on providing theoretical performance guarantees. We consider adversaries in both test and training time, and make the following contributions. First, we study the robustness of machine learning algorithms and model to test-time adversarial examples. We analyze the distributional and finite sample robustness of nearest neighbor classification, and propose a modified 1-Nearest-Neighbor classifier that both has theoretical guarantee and empirical improvement in robustness. Second, we examine the robustness of malware detectors to program transformation. We propose novel attacks that evade existing detectors using program transformation, and then show program normalization as a provably robust defense against such transformation. Finally, we investigate data poisoning attacks and defenses for online learning, in which models update and predict over data stream in real-time. We show efficient attacks for general adversarial objectives, analyze the conditions for which filtering based defenses are effective, and provide practical guidance on choosing defense mechanisms and parameters.
Robust Machine Learning By Integrating Context
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Author : Chengzhi Mao
language : en
Publisher:
Release Date : 2023
Robust Machine Learning By Integrating Context written by Chengzhi Mao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
In the second part of the thesis, I then detail my work using external domain knowledge. I first introduce using causal structure from external domain knowledge to improve domain generalization robustness. I then show how the association of multiple tasks and regularization objectives helps robustness. In the final part of this dissertation, I show three works on trustworthy and reliable foundation models, a general-purpose model that will be the foundation for many AI applications. I show a framework that uses context to secure, interpret, and control foundation models.
Towards Robust Machine Learning For Health Applications
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Author : Lisa Eisenberg
language : en
Publisher:
Release Date : 2022
Towards Robust Machine Learning For Health Applications written by Lisa Eisenberg and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.