Robust Machine Learning Algorithms And Systems For Detection And Mitigation Of Adversarial Attacks And Anomalies

Author : National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Computer Science and Telecommunications Board,Board on Mathematical Sciences and Analytics,Intelligence Community Studies Board
Publisher : National Academies Press
Page : 83 pages
File Size : 44,6 Mb
Release : 2019-08-22
Category : Computers
ISBN : 9780309496094

Get Book

Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies by National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Computer Science and Telecommunications Board,Board on Mathematical Sciences and Analytics,Intelligence Community Studies Board Pdf

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 : Yevgeniy Tu,Murat Shi
Publisher : Springer Nature
Page : 152 pages
File Size : 47,7 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031015809

Get Book

Adversarial Machine Learning by Yevgeniy Tu,Murat Shi Pdf

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,Cho-Jui Hsieh
Publisher : Academic Press
Page : 300 pages
File Size : 45,6 Mb
Release : 2022-08-20
Category : Computers
ISBN : 9780128242575

Get Book

Adversarial Robustness for Machine Learning by Pin-Yu Chen,Cho-Jui Hsieh Pdf

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

Author : Fei Hu,Xiali Hei
Publisher : CRC Press
Page : 347 pages
File Size : 48,6 Mb
Release : 2023-06-05
Category : Computers
ISBN : 9781000878875

Get Book

AI, Machine Learning and Deep Learning by Fei Hu,Xiali Hei Pdf

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 : Fuwei Li,Lifeng Lai,Shuguang Cui
Publisher : Springer Nature
Page : 109 pages
File Size : 53,9 Mb
Release : 2022-11-14
Category : Computers
ISBN : 9783031163753

Get Book

Machine Learning Algorithms by Fuwei Li,Lifeng Lai,Shuguang Cui Pdf

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,Xinghao Yang,Bo Liu,Wei Liu,Wanlei Zhou
Publisher : Springer Nature
Page : 316 pages
File Size : 42,9 Mb
Release : 2023-03-06
Category : Computers
ISBN : 9783030997724

Get Book

Adversarial Machine Learning by Aneesh Sreevallabh Chivukula,Xinghao Yang,Bo Liu,Wei Liu,Wanlei Zhou Pdf

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 : Frank J. Furrer
Publisher : Springer Nature
Page : 559 pages
File Size : 40,6 Mb
Release : 2022-07-20
Category : Computers
ISBN : 9783658371821

Get Book

Safety and Security of Cyber-Physical Systems by Frank J. Furrer Pdf

Cyber-physical systems (CPSs) consist of software-controlled computing devices communicating with each other and interacting with the physical world through sensors and actuators. Because most of the functionality of a CPS is implemented in software, the software is of crucial importance for the safety and security of the CPS. This book presents principle-based engineering for the development and operation of dependable software. The knowledge in this book addresses organizations that want to strengthen their methodologies to build safe and secure software for mission-critical cyber-physical systems. The book: • Presents a successful strategy for the management of vulnerabilities, threats, and failures in mission-critical cyber-physical systems; • Offers deep practical insight into principle-based software development (62 principles are introduced and cataloged into five categories: Business & organization, general principles, safety, security, and risk management principles); • Provides direct guidance on architecting and operating dependable cyber-physical systems for software managers and architects.

Author : Anthony D. Joseph,Blaine Nelson,Benjamin I. P. Rubinstein,J. D. Tygar
Publisher : Cambridge University Press
Page : 341 pages
File Size : 50,5 Mb
Release : 2019-02-21
Category : Computers
ISBN : 9781107043466

Get Book

Adversarial Machine Learning by Anthony D. Joseph,Blaine Nelson,Benjamin I. P. Rubinstein,J. D. Tygar Pdf

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.

Author : Dr Juan Lopez Jr,Dr Kalyan Perumalla,Dr Ambareen Siraj
Publisher : Academic Conferences Limited
Page : 128 pages
File Size : 51,5 Mb
Release : 2021-02-25
Category : History
ISBN : 9781912764884

Get Book

16th International Conference on Cyber Warfare and Security by Dr Juan Lopez Jr,Dr Kalyan Perumalla,Dr Ambareen Siraj Pdf

These proceedings represent the work of contributors to the 16th International Conference on Cyber Warfare and Security (ICCWS 2021), hosted by joint collaboration of Tennessee Tech Cybersecurity Education, Research and Outreach Center (CEROC), Computer Science department and the Oak Ridge National Laboratory, Tennessee on 25-26 February 2021. The Conference Co-Chairs are Dr. Juan Lopez Jr, Oak Ridge National Laboratory, Tennessee, and Dr. Ambareen Siraj, Tennessee Tech’s Cybersecurity Education, Research and Outreach Center (CEROC), and the Program Chair is Dr. Kalyan Perumalla, from Oak Ridge National Laboratory, Tennessee.

Author : Hamid Jahankhani,Arshad Jamal,Shaun Lawson
Publisher : Springer Nature
Page : 463 pages
File Size : 51,8 Mb
Release : 2021-05-20
Category : Computers
ISBN : 9783030685348

Get Book

Cybersecurity, Privacy and Freedom Protection in the Connected World by Hamid Jahankhani,Arshad Jamal,Shaun Lawson Pdf

This book provides an opportunity for investigators, government officials, systems scientists, strategists, assurance researchers, owners, operators and maintainers of large, complex and advanced systems and infrastructures to update their knowledge with the state of best practice in the challenging domains whilst networking with the leading representatives, researchers and solution providers. Drawing on 12 years of successful events on information security, digital forensics and cyber-crime, the 13th ICGS3-20 conference aims to provide attendees with an information-packed agenda with representatives from across the industry and the globe. The challenges of complexity, rapid pace of change and risk/opportunity issues associated with modern products, systems, special events and infrastructures. In an era of unprecedented volatile, political and economic environment across the world, computer-based systems face ever more increasing challenges, disputes and responsibilities, and whilst the Internet has created a global platform for the exchange of ideas, goods and services, it has also created boundless opportunities for cyber-crime. As an increasing number of large organizations and individuals use the Internet and its satellite mobile technologies, they are increasingly vulnerable to cyber-crime threats. It is therefore paramount that the security industry raises its game to combat these threats. Whilst there is a huge adoption of technology and smart home devices, comparably, there is a rise of threat vector in the abuse of the technology in domestic violence inflicted through IoT too. All these are an issue of global importance as law enforcement agencies all over the world are struggling to cope.

Author : David J. Miller,Zhen Xiang,George Kesidis
Publisher : Cambridge University Press
Page : 376 pages
File Size : 46,5 Mb
Release : 2023-08-31
Category : Computers
ISBN : 9781009315654

Get Book

Adversarial Learning and Secure AI by David J. Miller,Zhen Xiang,George Kesidis Pdf

Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naïve Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students.

Author : Ferhat Ozgur Catak
Publisher : Unknown
Page : 300 pages
File Size : 41,7 Mb
Release : 2021-10-30
Category : Electronic
ISBN : 1799890635

Get Book

Cyber Security and Adversarial Machine Learning by Ferhat Ozgur Catak Pdf

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.

Author : Xiaofeng Chen,Willy Susilo,Elisa Bertino
Publisher : Springer Nature
Page : 168 pages
File Size : 50,5 Mb
Release : 2021-07-02
Category : Computers
ISBN : 9789813367265

Get Book

Cyber Security Meets Machine Learning by Xiaofeng Chen,Willy Susilo,Elisa Bertino Pdf

Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

Author : Rachid Guerraoui
Publisher : Springer Nature
Page : 180 pages
File Size : 41,9 Mb
Release : 2024-07-02
Category : Electronic
ISBN : 9789819706884

Get Book

Robust Machine Learning by Rachid Guerraoui Pdf

Author : Alessandro Parisi
Publisher : Packt Publishing Ltd
Page : 331 pages
File Size : 42,9 Mb
Release : 2019-08-02
Category : Computers
ISBN : 9781789805178

Get Book

Hands-On Artificial Intelligence for Cybersecurity by Alessandro Parisi Pdf

Build smart cybersecurity systems with the power of machine learning and deep learning to protect your corporate assets Key FeaturesIdentify and predict security threats using artificial intelligenceDevelop intelligent systems that can detect unusual and suspicious patterns and attacksLearn how to test the effectiveness of your AI cybersecurity algorithms and toolsBook Description Today's organizations spend billions of dollars globally on cybersecurity. Artificial intelligence has emerged as a great solution for building smarter and safer security systems that allow you to predict and detect suspicious network activity, such as phishing or unauthorized intrusions. This cybersecurity book presents and demonstrates popular and successful AI approaches and models that you can adapt to detect potential attacks and protect your corporate systems. You'll learn about the role of machine learning and neural networks, as well as deep learning in cybersecurity, and you'll also learn how you can infuse AI capabilities into building smart defensive mechanisms. As you advance, you'll be able to apply these strategies across a variety of applications, including spam filters, network intrusion detection, botnet detection, and secure authentication. By the end of this book, you'll be ready to develop intelligent systems that can detect unusual and suspicious patterns and attacks, thereby developing strong network security defenses using AI. What you will learnDetect email threats such as spamming and phishing using AICategorize APT, zero-days, and polymorphic malware samplesOvercome antivirus limits in threat detectionPredict network intrusions and detect anomalies with machine learningVerify the strength of biometric authentication procedures with deep learningEvaluate cybersecurity strategies and learn how you can improve themWho this book is for If you’re a cybersecurity professional or ethical hacker who wants to build intelligent systems using the power of machine learning and AI, you’ll find this book useful. Familiarity with cybersecurity concepts and knowledge of Python programming is essential to get the most out of this book.