Download or read book Characterizing the Limits and Defenses of Machine Learning in Adversarial Settings written by Nicolas Papernot and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as object recognition, autonomous systems, security diagnostics, and playing the game of Go. Machine learning is not only a new paradigm for building software and systems, it is bringing social disruption at scale. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical communitys understanding of the nature and extent of these vulnerabilities remains limited. In this thesis, I focus my study on the integrity of ML models. Integrity refers here to the faithfulness of model predictions with respect to an expected outcome. This property is at the core of traditional machine learning evaluation, as demonstrated by the pervasiveness of metrics such as accuracy among practitioners. A large fraction of ML techniques were designed for benign execution environments. Yet, the presence of adversaries may invalidate some of these underlying assumptions by forcing a mismatch between the distributions on which the model is trained and tested. As ML is increasingly applied and being relied on for decision-making in critical applications like transportation or energy, the models produced are becoming a target for adversaries who have a strong incentive to force ML to mispredict. I explore the space of attacks against ML integrity at test time. Given full or limited access to a trained model, I devise strategies that modify the test data to create a worst-case drift between the training and test distributions. The implications of this part of my research is that an adversary with very weak access to a system, and little knowledge about the ML techniques it deploys, can nevertheless mount powerful attacks against such systems as long as she has the capability of interacting with it as an oracle: i.e., send inputs of the adversarys choice and observe the ML prediction. This systematic exposition of the poor generalization of ML models indicates the lack of reliable confidence estimates when the model is making predictions far from its training data. Hence, my efforts to increase the robustness of models to these adversarial manipulations strive to decrease the confidence of predictions made far from the training distribution. Informed by my progress on attacks operating in the black-box threat model, I first identify limitations to two defenses: defensive distillation and adversarial training. I then describe recent defensive efforts addressing these shortcomings. To this end, I introduce the Deep k-Nearest Neighbors classifier, which augments deep neural networks with an integrity check at test time. The approach compares internal representations produced by the deep neural network on test data with the ones learned on its training points. Using the labels of training points whose representations neighbor the test input across the deep neural networks layers, I estimate the nonconformity of the prediction with respect to the models training data. An application of conformal prediction methodology then paves the way for more reliable estimates of the models prediction credibility, i.e., how well the prediction is supported by training data. In turn, we distinguish legitimate test data with high credibility from adversarial data with low credibility. This research calls for future efforts to investigate the robustness of individual layers of deep neural networks rather than treating the model as a black-box. This aligns well with the modular nature of deep neural networks, which orchestrate simple computations to model complex functions. This also allows us to draw connections to other areas like interpretability in ML, which seeks to answer the question of: How can we provide an explanation for the model prediction to a human? Another by-product of this research direction is that I better distinguish vulnerabilities of ML models that are a consequence of the ML algorithms from those that can be explained by artifacts in the data.

Download or read book Machine Learning in Adversarial Settings written by Hossein Hosseini and published by . This book was released on 2019 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep neural networks have achieved remarkable success over the last decade in a variety of tasks. Such models are, however, typically designed and developed with the implicit assumption that they will be deployed in benign settings. With the increasing use of learning systems in security-sensitive and safety-critical application, such as banking, medical diagnosis, and autonomous cars, it is important to study and evaluate their performance in adversarial settings. The security of machine learning systems has been studied from different perspectives. Learning models are subject to attacks at both training and test phases. The main threat at test time is evasion attack, in which the attacker subtly modifies input data such that a human observer would perceive the original content, but the model generates different outputs. Such inputs, known as adversarial examples, has been used to attack voice interfaces, face-recognition systems and text classifiers. The goal of this dissertation is to investigate the test-time vulnerabilities of machine learning systems in adversarial settings and develop robust defensive mechanisms. The dissertation covers two classes of models, 1) commercial ML products developed by Google, namely Perspective, Cloud Vision, and Cloud Video Intelligence APIs, and 2) state-of-the-art image classification algorithms. In both cases, we propose novel test-time attack algorithms and also present defense methods against such attacks.

Download or read book Adversarial Machine Learning written by Yevgeniy Vorobeychik and published by Springer. This book was released on 2018-08-08 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Adversarial and Uncertain Reasoning for Adaptive Cyber Defense written by Sushil Jajodia and published by Springer Nature. This book was released on 2019-08-30 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today’s cyber defenses are largely static allowing adversaries to pre-plan their attacks. In response to this situation, researchers have started to investigate various methods that make networked information systems less homogeneous and less predictable by engineering systems that have homogeneous functionalities but randomized manifestations. The 10 papers included in this State-of-the Art Survey present recent advances made by a large team of researchers working on the same US Department of Defense Multidisciplinary University Research Initiative (MURI) project during 2013-2019. This project has developed a new class of technologies called Adaptive Cyber Defense (ACD) by building on two active but heretofore separate research areas: Adaptation Techniques (AT) and Adversarial Reasoning (AR). AT methods introduce diversity and uncertainty into networks, applications, and hosts. AR combines machine learning, behavioral science, operations research, control theory, and game theory to address the goal of computing effective strategies in dynamic, adversarial environments.

Download or read book Adversarial Machine Learning written by Aneesh Sreevallabh Chivukula and published by Springer Nature. This book was released on 2023-03-06 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.

Download or read book AI Machine Learning and Deep Learning written by Fei Hu and published by CRC Press. This book was released on 2023-06-05 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use. While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security). Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects: This is the first book to explain various practical attacks and countermeasures to AI systems Both quantitative math models and practical security implementations are provided It covers both "securing the AI system itself" and "using AI to achieve security" It covers all the advanced AI attacks and threats with detailed attack models It provides multiple solution spaces to the security and privacy issues in AI tools The differences among ML and DL security and privacy issues are explained Many practical security applications are covered

Download or read book The Good the Bad and the Ugly written by Xiaoting Li and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks have been widely adopted to address different real-world problems. Despite the remarkable achievements in machine learning tasks, they remain vulnerable to adversarial examples that are imperceptible to humans but can mislead the state-of-the-art models. More specifically, such adversarial examples can be generalized to a variety of common data structures, including images, texts and networked data. Faced with the significant threat that adversarial attacks pose to security-critical applications, in this thesis, we explore the good, the bad and the ugly of adversarial machine learning. In particular, we focus on the investigation on the applicability of adversarial attacks in real-world scenarios for social good and their defensive paradigms. The rapid progress of adversarial attacking techniques aids us to better understand the underlying vulnerabilities of neural networks that inspires us to explore their potential usage for good purposes. In real world, social media has extremely reshaped our daily life due to their worldwide accessibility, but its data privacy also suffers from inference attacks. Based on the fact that deep neural networks are vulnerable to adversarial examples, we attempt a novel perspective of protecting data privacy in social media and design a defense framework called Adv4SG, where we introduce adversarial attacks to forge latent feature representations and mislead attribute inference attacks. Considering that text data in social media shares the most significant privacy of users, we investigate how text-space adversarial attacks can be leveraged to protect users' attributes. Specifically, we integrate social media property to advance Adv4SG, and introduce cost-effective mechanisms to expedite attribute protection over text data under the black-box setting. By conducting extensive experiments on real-world social media datasets, we show that Adv4SG is an appealing method to mitigate the inference attacks. Second, we extend our study to more complex networked data. Social network is more of a heterogeneous environment which is naturally represented as graph-structured data, maintaining rich user activities and complicated relationships among them. This enables attackers to deploy graph neural networks (GNNs) to automate attribute inferences from user features and relationships, which makes such privacy disclosure hard to avoid. To address that, we take advantage of the vulnerability of GNNs to adversarial attacks, and propose a new graph poisoning attack, called AttrOBF to mislead GNNs into misclassification and thus protect personal attribute privacy against GNN-based inference attacks on social networks. AttrOBF provides a more practical formulation through obfuscating optimal training user attribute values for real-world social graphs. Our results demonstrate the promising potential of applying adversarial attacks to attribute protection on social graphs. Third, we introduce a watermarking-based defense strategy against adversarial attacks on deep neural networks. With the ever-increasing arms race between defenses and attacks, most existing defense methods ignore fact that attackers can possibly detect and reproduce the differentiable model, which leaves the window for evolving attacks to adaptively evade the defense. Based on this observation, we propose a defense mechanism that creates a knowledge gap between attackers and defenders by imposing a secret watermarking process into standard deep neural networks. We analyze the experimental results of a wide range of watermarking algorithms in our defense method against state-of-the-art attacks on baseline image datasets, and validate the effectiveness our method in protesting adversarial examples. Our research expands the investigation of enhancing the deep learning model robustness against adversarial attacks and unveil the insights of applying adversary for social good. We design Adv4SG and AttrOBF to take advantage of the superiority of adversarial attacking techniques to protect the social media user's privacy on the basis of discrete textual data and networked data, respectively. Both of them can be realized under the practical black-box setting. We also provide the first attempt at utilizing digital watermark to increase model's randomness that suppresses attacker's capability. Through our evaluation, we validate their effectiveness and demonstrate their promising value in real-world use.

Download or read book Adversary Aware Learning Techniques and Trends in Cybersecurity written by Prithviraj Dasgupta and published by Springer Nature. This book was released on 2021-01-22 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended to give researchers and practitioners in the cross-cutting fields of artificial intelligence, machine learning (AI/ML) and cyber security up-to-date and in-depth knowledge of recent techniques for improving the vulnerabilities of AI/ML systems against attacks from malicious adversaries. The ten chapters in this book, written by eminent researchers in AI/ML and cyber-security, span diverse, yet inter-related topics including game playing AI and game theory as defenses against attacks on AI/ML systems, methods for effectively addressing vulnerabilities of AI/ML operating in large, distributed environments like Internet of Things (IoT) with diverse data modalities, and, techniques to enable AI/ML systems to intelligently interact with humans that could be malicious adversaries and/or benign teammates. Readers of this book will be equipped with definitive information on recent developments suitable for countering adversarial threats in AI/ML systems towards making them operate in a safe, reliable and seamless manner.

Download or read book Cyber Security and Adversarial Machine Learning written by Ferhat Ozgur Catak and published by . This book was released on 2021-10-30 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on learning vulnerabilities and cyber security. The book gives detail on the new threats and mitigation methods in the cyber security domain, and provides information on the new threats in new technologies such as vulnerabilities in deep learning, data privacy problems with GDPR, and new solutions.

Download or read book DYNAMIC DEFENSES AND THE TRANSFERABILITY OF ADVERSARIAL EXAMPLES written by Samuel H Thomas and published by . This book was released on 2019 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adversarial machine learning has been an important area of study for the securing of machine learning systems. However, for every defense that is made to protect these artificial learners, a more sophisticated attack emerges to defeat it. This has created an arms race, with the problem of adversarial attacks never being fully mitigated. This thesis examines the field of adversarial machine learning; specifically, the property of transferability, and the use of dynamic defenses as a solution to attacks which leverage it. We show that this is an emerging field of research, which may be the solution to one of the most intractable problems in adversarial machine learning. We go on to implement a minimal experiment, demonstrating that research within this area is easily accessible. Finally, we address some of the hurdles to overcome in order to unify the disparate aspects of current related research.

Download or read book Interpretable Machine Learning for Malware Detection and Adversarial Defense written by Qi Li and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "As the Internet becomes ubiquitous and of paramount importance to people's lives, cyber attacks have also become a larger concern for not only individuals, but corporations and governments as well. As a consequence, cybersecurity has caught considerable attention. As machine learning has grown and large datasets have been amassed in recent years, an increasing number of advanced machine learning-based methods have been applied in the cybersecurity field. This proposed research focuses on proposing novel machine learning solutions with high performance for two cybersecurity tasks: malware detection and black-box adversarial defense. To help users of the prospective solutions gain insights into the cases and validate the outputs, the interpretability of the machine learning solutions is also evaluated in this research. All in all, this thesis makes new contributions in three directions: interpretable classification, malware detection, and black-box adversarial defense.In recent years increasingly complex deep neural networks have been proposed to refresh the classification performance scoreboard in the field of applied machine learning. However, it is difficult to understand exactly how they make predictions. In some cases, interpretability is expected for multiple reasons, such as to gain trust in the classification results and to gain knowledge from the explanations. For interpretable classification, we propose an intrinsically interpretable feedforward neural network architecture that achieves both solid classification performance and interpretability. Malware has been the major means for cyber attacks and there is tremendous growth in the volume of new malware broadcasting on the Internet. Thus, there is a pressing need to create intelligent malware detection systems. Existing malware detection methods lack the ability to analyze malware based on their complete assembly code, and state-of-the-art methods also lack interpretability for classification results. To address these limitations, we propose a novel state-of-the-art deep neural network architecture that can model the full semantics of assembly code and that has the ability to analyze a sample from multiple static feature scopes as well as the interpretability to explain the detection results.Machine learning models can be compromised by adversarial attacks that intend to cause them to mis-classify samples that contain often imperceptible but carefully selected perturbations. This vulnerability could be exploited to induce catastrophic consequences. Adversarial attacks can be conducted in either the while-box scenario, in which an adversary has complete knowledge of the target machine learning model, or the black-box scenario, in which an adversary has no knowledge of the target machine learning model. The latter is more common in real-world situations because most classification service providers do not reveal the details of their systems. Hence, our focus is on defense against black-box adversarial attacks. Existing defense methods are static and cannot dynamically evolve to adapt to adversarial attacks, which unnecessarily disadvantages them. In this segment of our research, we propose a novel dynamic defense method that can effectively utilize previous experience to identify black-box attacks"--

Download or read book Malware Detection written by Mihai Christodorescu and published by Springer Science & Business Media. This book was released on 2007-03-06 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Using Machine Learning in Adversarial Environments written by and published by . This book was released on 2016 with total page 3 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intrusion/anomaly detection systems are among the first lines of cyber defense. Commonly, they either use signatures or machine learning (ML) to identify threats, but fail to account for sophisticated attackers trying to circumvent them. We propose to embed machine learning within a game theoretic framework that performs adversarial modeling, develops methods for optimizing operational response based on ML, and integrates the resulting optimization codebase into the existing ML infrastructure developed by the Hybrid LDRD. Our approach addresses three key shortcomings of ML in adversarial settings: 1) resulting classifiers are typically deterministic and, therefore, easy to reverse engineer; 2) ML approaches only address the prediction problem, but do not prescribe how one should operationalize predictions, nor account for operational costs and constraints; and 3) ML approaches do not model attackers' response and can be circumvented by sophisticated adversaries. The principal novelty of our approach is to construct an optimization framework that blends ML, operational considerations, and a model predicting attackers reaction, with the goal of computing optimal moving target defense. One important challenge is to construct a realistic model of an adversary that is tractable, yet realistic. We aim to advance the science of attacker modeling by considering game-theoretic methods, and by engaging experimental subjects with red teaming experience in trying to actively circumvent an intrusion detection system, and learning a predictive model of such circumvention activities. In addition, we will generate metrics to test that a particular model of an adversary is consistent with available data.

Download or read book Robust Filtering Schemes for Machine Learning Systems to Defend Adversarial Attack written by Kishor Datta Gupta and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Defenses against adversarial attacks are essential to ensure the reliability of machine learning models as their applications are expanding in different domains. Existing ML defense techniques have several limitations in practical use. I proposed a trustworthy framework that employs an adaptive strategy to inspect both inputs and decisions. In particular, data streams are examined by a series of diverse filters before sending to the learning system and then crossed checked its output through a diverse set of filters before making the final decision. My experimental results illustrated that the proposed active learning-based defense strategy could mitigate adaptive or advanced adversarial manipulations both in input and after with the model decision for a wide range of ML attacks by higher accuracy. Moreover, the output decision boundary inspection using a classification technique automatically reaffirms the reliability and increases the trustworthiness of any ML-Based decision support system. Unlike other defense strategies, my defense technique does not require adversarial sample generation, and updating the decision boundary for detection makes the defense systems robust to traditional adaptive attacks..

Download or read book Designing Deep Networks for Adversarial Robustness and Security written by Kaleel Mahmood and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The advent of adversarial machine learning fundamentally challenges the widespread adoption of Convolutional Neural Networks (CNNs), Vision Transformers and other deep neural networks. Addressing adversarial machine learning attacks are of paramount importance to ensure such systems can be safely deployed in sensitive areas like health care and security. In this dissertation, we focus on developing three key concepts in adversarial machine learning: defense analysis for CNNs, defense design for CNNs and the robustness of the new Vision Transformer architecture. From the analysis side, we develop a new adaptive black-box attack and test eight recent defenses under this threat model. Next, we specifically focus on the black-box threat model and design a novel defense which oers significant improvements in robustness over state-of-the-art defenses. Lastly, we study the robustness of Vision Transformers, a new alternative to CNNs. We propose a new attack on Vision Transformers as well as a new CNN/transformer hybrid defense.

Download or read book Adversarial Machine Learning written by Anthony D. Joseph and published by Cambridge University Press. This book was released on 2019-02-21 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.

Download or read book Moving Target Defense written by Sushil Jajodia and published by Springer Science & Business Media. This book was released on 2011-08-26 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Moving Target Defense: Creating Asymmetric Uncertainty for Cyber Threats was developed by a group of leading researchers. It describes the fundamental challenges facing the research community and identifies new promising solution paths. Moving Target Defense which is motivated by the asymmetric costs borne by cyber defenders takes an advantage afforded to attackers and reverses it to advantage defenders. Moving Target Defense is enabled by technical trends in recent years, including virtualization and workload migration on commodity systems, widespread and redundant network connectivity, instruction set and address space layout randomization, just-in-time compilers, among other techniques. However, many challenging research problems remain to be solved, such as the security of virtualization infrastructures, secure and resilient techniques to move systems within a virtualized environment, automatic diversification techniques, automated ways to dynamically change and manage the configurations of systems and networks, quantification of security improvement, potential degradation and more. Moving Target Defense: Creating Asymmetric Uncertainty for Cyber Threats is designed for advanced -level students and researchers focused on computer science, and as a secondary text book or reference. Professionals working in this field will also find this book valuable.