Author : National Academies of Sciences, Engineering, and Medicine
Publisher : National Academies Press
Page : 83 pages
File Size : 22,51 MB
Release : 2019-08-22
Category : Computers
ISBN : 0309496098
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.
Author : National Academies of Sciences, Engineering, and Medicine
Publisher : National Academies Press
Page : 83 pages
File Size : 35,61 MB
Release : 2019-08-22
Category : Computers
ISBN : 0309496128
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.
Author : Anthony D. Joseph
Publisher : Cambridge University Press
Page : 341 pages
File Size : 20,17 MB
Release : 2019-02-21
Category : Computers
ISBN : 1108325874
Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race.
Author : Fuwei Li
Publisher : Springer Nature
Page : 109 pages
File Size : 36,9 MB
Release : 2022-11-14
Category : Computers
ISBN : 3031163753
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.
Author : Aneesh Sreevallabh Chivukula
Publisher : Springer Nature
Page : 316 pages
File Size : 46,48 MB
Release : 2023-03-06
Category : Computers
ISBN : 3030997723
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.
Author : Clarence Chio
Publisher : "O'Reilly Media, Inc."
Page : 385 pages
File Size : 49,9 MB
Release : 2018-01-26
Category : Computers
ISBN : 1491979879
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions
Author : Yevgeniy Vorobeychik
Publisher :
Page : 152 pages
File Size : 44,88 MB
Release : 1901
Category : Game theory
ISBN : 9781681733982
General-Purpose Defense: Iterative Retraining -- Evasion-Robustness through Feature-Level Protection -- Decision Randomization -- Model -- Optimal Randomized Operational Use of Classification -- Evasion-Robust Regression -- Bibliographic Notes -- Data Poisoning Attacks -- Modeling Poisoning Attacks -- Poisoning Attacks on Binary Classification -- Label-Flipping Attacks -- Poison Insertion Attack on Kernel SVM -- Poisoning Attacks for Unsupervised Learning -- Poisoning Attacks on Clustering -- Poisoning Attacks on Anomaly Detection -- Poisoning Attack on Matrix Completion -- Attack Model -- Attacking Alternating Minimization -- Attacking Nuclear Norm Minimization -- Mimicking Normal User Behaviors -- A General Framework for Poisoning Attacks -- Black-Box Poisoning Attacks -- Bibliographic Notes -- Defending Against Data Poisoning -- Robust Learning through Data Sub-Sampling -- Robust Learning through Outlier Removal -- Robust Learning through Trimmed Optimization -- Robust Matrix Factorization -- Noise-Free Subspace Recovery -- Dealing with Noise -- Efficient Robust Subspace Recovery -- An Efficient Algorithm for Trimmed Optimization Problems -- Bibliographic Notes -- Attacking and Defending Deep Learning -- Attacking and Defending Deep Learning -- Neural Network Models -- Attacks on Deep Neural Networks: Adversarial Examples -- l_2-Norm Attacks -- l_-Norm Attacks -- l_0-Norm Attacks -- Attacks in the Physical World -- Black-Box Attacks -- Making Deep Learning Robust to Adversarial Examples -- Robust Optimization -- Retraining -- Distillation -- Bibliographic Notes -- The Road Ahead -- Beyond Robust Optimization -- Incomplete Information -- Confidence in Predictions -- Randomization -- Multiple Learners -- Models and Validation -- Bibliography -- Authors' Biographies -- Index -- Blank Page.
Author : Fei Hu
Publisher : CRC Press
Page : 347 pages
File Size : 16,52 MB
Release : 2023-06-05
Category : Computers
ISBN : 1000878872
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
Author : Ali Abdullah Sayghe
Publisher :
Page : 0 pages
File Size : 42,66 MB
Release : 2023
Category : Electrical engineering
ISBN :
Over the last decade, the number of cyberattacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, False Data Injection Attacks (FDIAs) is a class of cyberattacks against power grid monitoring systems. Adversaries can successfully perform FDIAs in order to manipulate the power system State Estimation (SE) by compromising sensors or modifying system data. SE is an essential process performed by the Energy Management System (EMS) towards estimating unknown state variables based on system redundant measurements and network topology. SE routines include Bad Data Detection (BDD) algorithms to eliminate errors from the acquired measurements, e.g., in case of sensor failures. FDIAs can bypass BDD modules to inject malicious data vectors into a subset of measurements without being detected; and thus, manipulate the results of the SE process. In order to overcome the limitations of traditional residual-based BDD approaches, data-driven solutions based on machine learning algorithms have been widely adopted for detecting malicious manipulation of sensor data due to their fast execution times and accurate results. Machine learning algorithms have been proposed as a promising solution for detecting FDIAs, as they can automatically learn patterns and anomalies in the data that are indicative of an attack. However, these algorithms are also vulnerable to adversarial examples, which are maliciously crafted inputs that are designed to mislead the model into making a wrong decision. In this dissertation, we focus on evaluating the vulnerability of machine learning algorithms against adversarial examples in the context of FDIAs. Specifically, we study six different cases of adversarial attacks, including Adversarial Label Flipped Attack on SVM, Targeted Fast Gradient Sign Method Attack on MLP, Limited-memory BFGS Attack on MLP, Jacobian-based Saliency Attack on MLP, Carlini and Wagner Adversarial Attack, and Zeroth Order Optimization-based Attack. We implement these attacks on a simulated power system, and evaluate the performance of the machine learning algorithms in detecting them. The results of this study provide insights into the strengths and weaknesses of different machine learning algorithms in detecting FDIAs and adversarial examples. We also provide recommendations on how to improve the robustness of these algorithms against adversarial examples. The findings of this research are useful for practitioners in the field of power systems and machine learning, as well as for researchers working on the security of cyber-physical systems. This dissertation is organized into several chapters, starting with background, literature review, objective, adversarial examples, adversarial examples on power systems state estimation, evasion attacks with adversarial deep learning against power system state estimation, adversarial machine learning designs against learning-based attack detection algorithms in power systems and a summary of the work and future work.
Author : Rajvardhan Oak
Publisher : Packt Publishing Ltd
Page : 330 pages
File Size : 29,13 MB
Release : 2023-05-31
Category : Computers
ISBN : 1804611972
Work on 10 practical projects, each with a blueprint for a different machine learning technique, and apply them in the real world to fight against cybercrime Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn how to frame a cyber security problem as a machine learning problem Examine your model for robustness against adversarial machine learning Build your portfolio, enhance your resume, and ace interviews to become a cybersecurity data scientist Book Description Machine learning in security is harder than other domains because of the changing nature and abilities of adversaries, high stakes, and a lack of ground-truth data. This book will prepare machine learning practitioners to effectively handle tasks in the challenging yet exciting cybersecurity space. The book begins by helping you understand how advanced ML algorithms work and shows you practical examples of how they can be applied to security-specific problems with Python – by using open source datasets or instructing you to create your own. In one exercise, you'll also use GPT 3.5, the secret sauce behind ChatGPT, to generate an artificial dataset of fabricated news. Later, you'll find out how to apply the expert knowledge and human-in-the-loop decision-making that is necessary in the cybersecurity space. This book is designed to address the lack of proper resources available for individuals interested in transitioning into a data scientist role in cybersecurity. It concludes with case studies, interview questions, and blueprints for four projects that you can use to enhance your portfolio. By the end of this book, you'll be able to apply machine learning algorithms to detect malware, fake news, deep fakes, and more, along with implementing privacy-preserving machine learning techniques such as differentially private ML. What you will learn Use GNNs to build feature-rich graphs for bot detection and engineer graph-powered embeddings and features Discover how to apply ML techniques in the cybersecurity domain Apply state-of-the-art algorithms such as transformers and GNNs to solve security-related issues Leverage ML to solve modern security issues such as deep fake detection, machine-generated text identification, and stylometric analysis Apply privacy-preserving ML techniques and use differential privacy to protect user data while training ML models Build your own portfolio with end-to-end ML projects for cybersecurity Who this book is for This book is for machine learning practitioners interested in applying their skills to solve cybersecurity issues. Cybersecurity workers looking to leverage ML methods will also find this book useful. An understanding of the fundamental machine learning concepts and beginner-level knowledge of Python programming are needed to grasp the concepts in this book. Whether you're a beginner or an experienced professional, this book offers a unique and valuable learning experience that'll help you develop the skills needed to protect your network and data against the ever-evolving threat landscape.
