Author: Shufei Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages :

Get Book Here

Book Description

Author: Mihai Christodorescu
Publisher: Springer Science & Business Media
ISBN: 0387445994
Category : Computers
Languages : en
Pages : 307

Get Book Here

Book Description
This book captures the state of the art research in the area of malicious code detection, prevention and mitigation. It contains cutting-edge behavior-based techniques to analyze and detect obfuscated malware. The book analyzes current trends in malware activity online, including botnets and malicious code for profit, and it proposes effective models for detection and prevention of attacks using. Furthermore, the book introduces novel techniques for creating services that protect their own integrity and safety, plus the data they manage.

Author: Minhao Cheng
Publisher:
ISBN:
Category :
Languages : en
Pages : 158

Get Book Here

Book Description
Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples. That is, a slightly modified example could be easily generated and fool a well-trained image classifier based on deep neural networks (DNNs) with high confidence. This makes it difficult to apply neural networks in security-critical areas. To find such examples, we first introduce and define adversarial examples. In the first part, we then discuss how to build adversarial attacks in both image and discrete domains. For image classification, we introduce how to design an adversarial attacker in three different settings. Among them, we focus on the most practical setup for evaluating the adversarial robustness of a machine learning system with limited access: the hard-label black-box attack setting for generating adversarial examples, where limited model queries are allowed and only the decision is provided to a queried data input. For the discrete domain, we first talk about its difficulty and introduce how to conduct the adversarial attack on two applications. While crafting adversarial examples is an important technique to evaluate the robustness of DNNs, there is a huge need for improving the model robustness as well. Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems. In the second part, we talk about the methods to strengthen the model's adversarial robustness. We first discuss attack-dependent defense. Specifically, we first discuss one of the most effective methods for improving the robustness of neural networks: adversarial training and its limitations. We introduce a variant to overcome its problem. Then we take a different perspective and introduce attack-independent defense. We summarize the current methods and introduce a framework-based vicinal risk minimization. Inspired by the framework, we introduce self-progressing robust training. Furthermore, we discuss the robustness trade-off problem and introduce a hypothesis and propose a new method to alleviate it.

Author: Andras Rozsa
Publisher:
ISBN:
Category : Machine learning
Languages : en
Pages : 150

Get Book Here

Book Description
One of the greatest technological advancements of the 21st century has been the rise of machine learning. This thriving field of research already has a great impact on our lives and, considering research topics and the latest advancements, will continue to rapidly grow. In the last few years, the most powerful machine learning models have managed to reach or even surpass human level performance on various challenging tasks, including object or face recognition in photographs. Although we are capable of designing and training machine learning models that perform extremely well, the intriguing discovery of adversarial examples challenges our understanding of these models and raises questions about their real-world applications. That is, vulnerable machine learning models misclassify examples that are indistinguishable from correctly classified examples by human observers. Furthermore, in many cases a variety of machine learning models having different architectures and/or trained on different subsets of training data misclassify the same adversarial example formed by an imperceptibly small perturbation. In this dissertation, we mainly focus on adversarial examples and closely related research areas such as quantifying the quality of adversarial examples in terms of human perception, proposing algorithms for generating adversarial examples, and analyzing the cross-model generalization properties of such examples. We further explore the robustness of facial attribute recognition and biometric face recognition systems to adversarial perturbations, and also investigate how to alleviate the intriguing properties of machine learning models.

Author: Qinglong Wang
Publisher:
ISBN:
Category :
Languages : en
Pages :

Get Book Here

Book Description
"Recent years witnessed the successful resurgence of neural networks through the lens of deep learning research. As the spread of deep neural network (DNN) continues to reach multifarious branches of research, including computer vision, natural language processing, and malware detection, it has been found that the vulnerability of these powerful models is equally impressive as their capability in classification tasks. Specifically, research on the adversarial example problem exposes that DNNs, albeit powerful when confronted with legitimate samples, suffer severely from adversarial examples. These synthetic examples can be created by slightly modifying legitimate samples. We speculate that this vulnerability may significantly impede an extensive adoption of DNNs in safety-critical domains. This thesis aims to comprehend some of the mysteries of this vulnerability of DNN, design generic frameworks and deployable algorithms to protect DNNs with different architectures from attacks armed with adversarial examples. We first conduct a thorough exploration of existing research on explaining the pervasiveness of adversarial examples. We unify the hypotheses raised in existing work by extracting three major influencing factors, i.e., data, model, and training. These factors are also helpful in locating different attack and defense methods proposed in the research spectrum and analyzing their effectiveness and limitations. Then we perform two threads of research on neural networks with feed-forward and recurrent architectures, respectively. In the first thread, we focus on the adversarial robustness of feed-forward neural networks, which have been widely applied to process images. Under our proposed generic framework, we design two types of adversary resistant feed-forward networks that weaken the destructive power of adversarial examples and even prevent their creation. We theoretically validate the effectiveness of our methods and empirically demonstrate that they significantly boost a DNN's adversarial robustness while maintaining high accuracy in classification. Our second thread of study focuses on the adversarial robustness of the recurrent neural network (RNN), which represents a variety of networks typically used for processing sequential data. We develop an evaluation framework and propose to quantitatively evaluate RNN's adversarial robustness with deterministic finite automata (DFA), which represent rigorous rules and can be extracted from RNNs, and a distance metric suitable for strings. We demonstrate the feasibility of using extracted DFA as rules through conducting careful experimental studies to identify key conditions that affect the extraction performance. Moreover, we theoretically establish the correspondence between different RNNs and different DFA, and empirically validate the correspondence by evaluating and comparing different RNNs for their extraction performance. At last, we develop an algorithm under our framework and conduct a case study to evaluate the adversarial robustness of different RNNs on a set of regular grammars"--

Author: Puyudi Yang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

Get Book Here

Book Description
Robustness to adversarial perturbation has become an extremely important criterion for applications of deep neural networks in many security-sensitive domains such as spam detection, fraud detection, criminal justice, malware detection, and financial markets. Thus, for a given model in security sensitive applications, the evaluation of its robustness to adversarial attacks are more and more crucial. In this thesis, we present a probabilistic framework for generating adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalable learning-based method, Gumbel Attack, that illustrate various trade-offs in the design of attacks. We also propose a meta algorithm called BOSH-attack that boost the performance of current decision-based attack algorithms under the hard-label black-box setting. Finally we develop a detection method to detect maliciously crafted adversarial examples via tools in model interpretation.

Author: Muchao Ye
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

Get Book Here

Book Description
Language models (LMs) built by deep neural networks (DNNs) have achieved great success in various areas of artificial intelligence, which have played an increasingly vital role in profound applications including chatbots and smart healthcare. Nonetheless, the vulnerability of DNNs against adversarial examples still threatens the application of neural LMs to safety-critical tasks. To specify, DNNs will change their correct predictions into incorrect ones when small perturbations are added to the original input texts. In this dissertation, we identify key challenges in evaluating and certifying the adversarial robustness of neural LMs and bridge those gaps through efficient hard-label text adversarial attacks and a unified certified robust training framework. The first step of developing neural LMs with high adversarial robustness is evaluating whether they are empirically robust against perturbed texts. The vital technique related to that is the text adversarial attack, which aims to construct a text that can fool LMs. Ideally, it shall output high-quality adversarial examples in a realistic setting with high efficiency. However, current evaluation pipelines proposed in the realistic hard-label setting adopt heuristic search methods, consequently meeting an inefficiency problem. To tackle this limitation, we introduce a series of hard-label text adversarial attack methods, which successfully tackle the inefficiency problem by using a pretrained word embedding space as an intermediate. A deep dive into this idea illustrates that utilizing an estimated decision boundary in the introduced word embedding space helps improve the quality of crafted adversarial examples. The ultimate goal of constructing robust neural LMs is obtaining ones for which adversarial examples do not exist, which can be realized through certified robust training. The research community has proposed different types of certified robust training either in the discrete input space or in the continuous latent feature space. We discover the structural gap within current pipelines and unify them in the word embedding space. By removing unnecessary bound computation modules, i.e., interval bound propagation, and harnessing a new decoupled regularization learning paradigm, our unification can provide a stronger robustness guarantee. Given the aforementioned contributions, we believe our findings will help contribute to the development of robust neural LMs.

Author: Mehrgan Khoshpasand Foumani
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

Get Book Here

Book Description
Adversarial examples are inputs designed by an adversary to fool the machine learning models. Despite having overly simplifying assumptions and extensive studies in recent years, there is no defense against adversarial examples for complex tasks (e.g., ImageNet). However, for more straightforward tasks like hand-written digit classification, a robust model seems to be within reach. Having the right estimation of the adversarial robustness of the models has been one of the main challenges researchers face. First, we present AETorch, a Pytorch library containing the strongest attacks and the common threat models that make comparing the defenses easier. Most of the research about adversarial examples have focused on adding small l[subscript p] bounded perturbations to the natural inputs with the assumption that the actual label remains unchanged. However, being robust in l[subscript p] bounded settings does not guarantee general robustness. Additionally, we present an efficient technique to create unrestricted adversarial examples using generative adversarial networks on MNIST, SVHN, and fashion-mnist datasets. We demonstrate that even the state-of-the-art adversarially robust MNIST classifiers are vulnerable to the adversarial examples generated with this technique. We demonstrate that our method is better than the previous unrestricted techniques since it has access to a bigger adversarial subspace. Additionally, we show that examples generated with our method are transferable. Overall, our findings emphasize the need for further studying the vulnerability of neural networks to unrestricted adversarial examples.

Author: Yevgeniy Tu
Publisher: Springer Nature
ISBN: 3031015800
Category : Computers
Languages : en
Pages : 152

Get Book Here

Book Description
The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research. Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.

Author: Pin-Yu Chen
Publisher: Elsevier
ISBN: 0128240202
Category : Computers
Languages : en
Pages : 298

Get Book Here

Book Description
Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and veri?cation. Sections cover adversarial attack, veri?cation and defense, mainly focusing on image classi?cation 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