Attacks Defenses And Testing For Deep Learning
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Attacks Defenses And Testing For Deep Learning
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Author : Jinyin Chen
language : en
Publisher: Springer Nature
Release Date : 2024-06-03
Attacks Defenses And Testing For Deep Learning written by Jinyin Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-03 with Computers categories.
This book provides a systematic study on the security of deep learning. With its powerful learning ability, deep learning is widely used in CV, FL, GNN, RL, and other scenarios. However, during the process of application, researchers have revealed that deep learning is vulnerable to malicious attacks, which will lead to unpredictable consequences. Take autonomous driving as an example, there were more than 12 serious autonomous driving accidents in the world in 2018, including Uber, Tesla and other high technological enterprises. Drawing on the reviewed literature, we need to discover vulnerabilities in deep learning through attacks, reinforce its defense, and test model performance to ensure its robustness. Attacks can be divided into adversarial attacks and poisoning attacks. Adversarial attacks occur during the model testing phase, where the attacker obtains adversarial examples by adding small perturbations. Poisoning attacks occur during the model training phase, wherethe attacker injects poisoned examples into the training dataset, embedding a backdoor trigger in the trained deep learning model. An effective defense method is an important guarantee for the application of deep learning. The existing defense methods are divided into three types, including the data modification defense method, model modification defense method, and network add-on method. The data modification defense method performs adversarial defense by fine-tuning the input data. The model modification defense method adjusts the model framework to achieve the effect of defending against attacks. The network add-on method prevents the adversarial examples by training the adversarial example detector. Testing deep neural networks is an effective method to measure the security and robustness of deep learning models. Through test evaluation, security vulnerabilities and weaknesses in deep neural networks can be identified. By identifying and fixing these vulnerabilities, the security and robustness of the model can be improved. Our audience includes researchers in the field of deep learning security, as well as software development engineers specializing in deep learning.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Strengthening Deep Neural Networks
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Author : Katy Warr
language : en
Publisher: O'Reilly Media
Release Date : 2019-07-03
Strengthening Deep Neural Networks written by Katy Warr and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-03 with Computers categories.
As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come
Ai And Deep Learning For Networks
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Author : Gopee Mukhopadhyay
language : en
Publisher: Educohack Press
Release Date : 2025-02-20
Ai And Deep Learning For Networks written by Gopee Mukhopadhyay and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Computers categories.
Welcome to the forefront of technological evolution with "AI and Deep Learning for Networks." Our book is your definitive guide to understanding the powerful combination of AI and deep learning, simplifying complex concepts while providing the technical depth needed for meaningful comprehension. We explore the transformative power of AI, starting from foundational principles to cutting-edge applications in computer networks. Whether you're a curious beginner or an experienced professional, this book offers a seamless blend of accessible language and technical precision. Discover the intricacies of machine learning, the nuances of supervised and unsupervised learning, and the significance of fundamental algorithms like neural networks. Each chapter caters to a wide range of readers, ensuring everyone can unravel the symbiosis between intelligent algorithms and network dynamics. Dive deeper into the synergy of Deep Learning and Software Defined Networks, exploring how convolutional neural networks optimize traffic engineering and reinforcement learning enhances network security. Real-world applications, ethical considerations, and emerging trends are interwoven to provide a holistic understanding of AI in computer networking. This book is not just a manual but a companion on your journey to a future where intelligent networks seamlessly adapt, secure, and innovate. Embrace the transformative potential of AI and deep learning, and chart your course toward a technologically enriched future.
Adversarial Machine Learning
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Author : Aneesh Sreevallabh Chivukula
language : en
Publisher: Springer Nature
Release Date : 2023-03-06
Adversarial Machine Learning written by Aneesh Sreevallabh Chivukula and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-06 with Computers categories.
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.
Cybersecurity Privacy And Freedom Protection In The Connected World
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Author : Hamid Jahankhani
language : en
Publisher: Springer Nature
Release Date : 2021-05-20
Cybersecurity Privacy And Freedom Protection In The Connected World written by Hamid Jahankhani and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-20 with Computers categories.
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.
Computational Intelligence In Pattern Recognition
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Author : Asit Kumar Das
language : en
Publisher: Springer Nature
Release Date : 2025-05-26
Computational Intelligence In Pattern Recognition written by Asit Kumar Das and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-26 with Computers categories.
This book features high-quality research papers presented at the 6th International Conference on Computational Intelligence in Pattern Recognition (CIPR 2024), held at Maharaja Sriram Chandra Bhanja Deo University (MSCB University), Baripada, Odisha, India, during March 15–16, 2024. It includes practical development experiences in various areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics, and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments.
Handbook Of Power Electronics In Autonomous And Electric Vehicles
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Author : Muhammad H. Rashid
language : en
Publisher: Elsevier
Release Date : 2024-07-22
Handbook Of Power Electronics In Autonomous And Electric Vehicles written by Muhammad H. Rashid and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-22 with Technology & Engineering categories.
Handbook of Power Electronics in Autonomous and Electric Vehicles provides advanced knowledge on autonomous systems, electric propulsion in electric vehicles, radars and sensors for autonomous systems, and relevant aspects of energy storage and battery charging. The work is designed to provide clear technical presentation with a focus on commercial viability. It supports any and all aspects of a project requiring specialist design, analysis, installation, commissioning and maintenance services. With this book in hand, engineers will be able to execute design, analysis and evaluation of assigned projects using sound engineering principles and commercial requirements, policies, and product and program requirements. - Presents core power systems and engineering applications relevant to autonomous and electric vehicles in characteristic depth and technical presentation - Offers practical support and guidance with detailed examples and applications for laboratory vehicular test plans and automotive field experimentation - Includes modern technical coverage of emergent fields, including sensors and radars, battery charging and monitoring, and vehicle cybersecurity
Risk Assessment And Countermeasures For Cybersecurity
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Author : Almaiah, Mohammed Amin
language : en
Publisher: IGI Global
Release Date : 2024-05-01
Risk Assessment And Countermeasures For Cybersecurity written by Almaiah, Mohammed Amin and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-01 with Computers categories.
The relentless growth of cyber threats poses an escalating challenge to our global community. The current landscape of cyber threats demands a proactive approach to cybersecurity, as the consequences of lapses in digital defense reverberate across industries and societies. From data breaches to sophisticated malware attacks, the vulnerabilities in our interconnected systems are glaring. As we stand at the precipice of a digital revolution, the need for a comprehensive understanding of cybersecurity risks and effective countermeasures has never been more pressing. Risk Assessment and Countermeasures for Cybersecurity is a book that clarifies many of these challenges in the realm of cybersecurity. It systematically navigates the web of security challenges, addressing issues that range from cybersecurity risk assessment to the deployment of the latest security countermeasures. As it confronts the threats lurking in the digital shadows, this book stands as a catalyst for change, encouraging academic scholars, researchers, and cybersecurity professionals to collectively fortify the foundations of our digital world.
Intelligent Security Systems
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Author : Leon Reznik
language : en
Publisher: John Wiley & Sons
Release Date : 2021-09-23
Intelligent Security Systems written by Leon Reznik and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-23 with Computers categories.
INTELLIGENT SECURITY SYSTEMS Dramatically improve your cybersecurity using AI and machine learning In Intelligent Security Systems, distinguished professor and computer scientist Dr. Leon Reznik delivers an expert synthesis of artificial intelligence, machine learning and data science techniques, applied to computer security to assist readers in hardening their computer systems against threats. Emphasizing practical and actionable strategies that can be immediately implemented by industry professionals and computer device’s owners, the author explains how to install and harden firewalls, intrusion detection systems, attack recognition tools, and malware protection systems. He also explains how to recognize and counter common hacking activities. This book bridges the gap between cybersecurity education and new data science programs, discussing how cutting-edge artificial intelligence and machine learning techniques can work for and against cybersecurity efforts. Intelligent Security Systems includes supplementary resources on an author-hosted website, such as classroom presentation slides, sample review, test and exam questions, and practice exercises to make the material contained practical and useful. The book also offers: A thorough introduction to computer security, artificial intelligence, and machine learning, including basic definitions and concepts like threats, vulnerabilities, risks, attacks, protection, and tools An exploration of firewall design and implementation, including firewall types and models, typical designs and configurations, and their limitations and problems Discussions of intrusion detection systems (IDS), including architecture topologies, components, and operational ranges, classification approaches, and machine learning techniques in IDS design A treatment of malware and vulnerabilities detection and protection, including malware classes, history, and development trends Perfect for undergraduate and graduate students in computer security, computer science and engineering, Intelligent Security Systems will also earn a place in the libraries of students and educators in information technology and data science, as well as professionals working in those fields.