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 :
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 with categories.
Attacks Defenses And Testing For Deep Learning
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Author : Jinyin Chen
language : en
Publisher: Springer
Release Date : 2024-05-17
Attacks Defenses And Testing For Deep Learning written by Jinyin Chen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-17 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, where the 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.
Adversarial Learning And Secure Ai
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Author : David J. Miller
language : en
Publisher: Cambridge University Press
Release Date : 2023-08-31
Adversarial Learning And Secure Ai written by David J. Miller and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-31 with Computers categories.
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.
Defense Against Test Time Evasion Attacks And Backdoor Attacks
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Author : Hang Wang
language : en
Publisher:
Release Date : 2023
Defense Against Test Time Evasion Attacks And Backdoor Attacks written by Hang Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Deep Neural networks (DNN) have been successfully applied to many areas. However, they have been shown to be vulnerable to adversarial attacks. One representative adversarial attack is the test time evasion attack (TTE attack, also known as adversarial example attack), which modifies a test sample with a small, sample-specific, and human imperceptible perturbation so that it is misclassified by the DNN classifier. The backdoor attack (Trojan) is another type of adversarial attack emerging recently. A backdoor attacker aims to inject a backdoor trigger (typically a universal pattern) into an attacked DNN classifier, such that the classifier will misclassify a test sample into a pre-designed target class whenever the backdoor trigger is present. A backdoor attack can be launched either by poisoning the training dataset or by controlling the training process. Both types of attacks are very harmful, especially in high-risk applications (like facial recognition authorization and traffic sign recognition in self-driving cars) where misclassification will lead to serious consequences. Defending against those attacks is important and challenging. To defend against the TTE attack, one can either robustify the DNN or detect the adversarial examples. One can attempt to robustify a DNN through adversarial training, certified training, or DNN embedding. Also, some adversarial examples can be identified using the internal layer activation features. Defense against backdoor attacks can be mounted at different stages. Pre-training (or during training) defenses aim to obtain a clean model given the potentially poisoned training set. Post-training defenses aim to either detect if a model is attacked or repair a potentially poisoned model to avoid misclassifications. Inference time defenses aim to detect or robustly classify a test sample with the backdoor trigger. In this thesis, we propose several defenses against TTE attacks and backdoor attacks. For TTE attacks, we proposed a conditional generative adversarial network based anomaly detection method (ACGAN-ADA). For backdoor attacks, we proposed a pre-training data cleansing method based on a contrastive learning method, which can cleanse the training set by filtering and relabeling the out-of-distribution training samples. Several defense schemes are also proposed post-training: A maximum classification-margin based backdoor detection method (MM-BD) is proposed to detect whether a model is attacked. The MM-BD method is based on the observation that the attacked model will overfit to the backdoor trigger, and thus be overconfident in the decision made on a sample with the backdoor trigger. MM-BD makes no assumption about the backdoor pattern type.
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.
Machine Learning Techniques For Cybersecurity
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Author : Elisa Bertino
language : en
Publisher: Springer Nature
Release Date : 2023-04-08
Machine Learning Techniques For Cybersecurity written by Elisa Bertino and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-08 with Computers categories.
This book explores machine learning (ML) defenses against the many cyberattacks that make our workplaces, schools, private residences, and critical infrastructures vulnerable as a consequence of the dramatic increase in botnets, data ransom, system and network denials of service, sabotage, and data theft attacks. The use of ML techniques for security tasks has been steadily increasing in research and also in practice over the last 10 years. Covering efforts to devise more effective defenses, the book explores security solutions that leverage machine learning (ML) techniques that have recently grown in feasibility thanks to significant advances in ML combined with big data collection and analysis capabilities. Since the use of ML entails understanding which techniques can be best used for specific tasks to ensure comprehensive security, the book provides an overview of the current state of the art of ML techniques for security and a detailed taxonomy of security tasks and corresponding ML techniques that can be used for each task. It also covers challenges for the use of ML for security tasks and outlines research directions. While many recent papers have proposed approaches for specific tasks, such as software security analysis and anomaly detection, these approaches differ in many aspects, such as with respect to the types of features in the model and the dataset used for training the models. In a way that no other available work does, this book provides readers with a comprehensive view of the complex area of ML for security, explains its challenges, and highlights areas for future research. This book is relevant to graduate students in computer science and engineering as well as information systems studies, and will also be useful to researchers and practitioners who work in the area of ML techniques for security tasks.
Hacking Artificial Intelligence
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Author : Davey Gibian
language : en
Publisher: Rowman & Littlefield
Release Date : 2022-05-05
Hacking Artificial Intelligence written by Davey Gibian and has been published by Rowman & Littlefield this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-05 with Computers categories.
Sheds light on the ability to hack AI and the technology industry’s lack of effort to secure vulnerabilities. We are accelerating towards the automated future. But this new future brings new risks. It is no surprise that after years of development and recent breakthroughs, artificial intelligence is rapidly transforming businesses, consumer electronics, and the national security landscape. But like all digital technologies, AI can fail and be left vulnerable to hacking. The ability to hack AI and the technology industry’s lack of effort to secure it is thought by experts to be the biggest unaddressed technology issue of our time. Hacking Artificial Intelligence sheds light on these hacking risks, explaining them to those who can make a difference. Today, very few people—including those in influential business and government positions—are aware of the new risks that accompany automated systems. While society hurdles ahead with AI, we are also rushing towards a security and safety nightmare. This book is the first-ever layman’s guide to the new world of hacking AI and introduces the field to thousands of readers who should be aware of these risks. From a security perspective, AI is today where the internet was 30 years ago. It is wide open and can be exploited. Readers from leaders to AI enthusiasts and practitioners alike are shown how AI hacking is a real risk to organizations and are provided with a framework to assess such risks, before problems arise.
Ai Machine Learning And Deep Learning
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Author : Fei Hu
language : en
Publisher: CRC Press
Release Date : 2023-06-05
Ai Machine Learning And Deep Learning written by Fei Hu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-05 with Computers categories.
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
Robust Machine Learning Algorithms And Systems For Detection And Mitigation Of Adversarial Attacks And Anomalies
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Author : National Academies of Sciences, Engineering, and Medicine
language : en
Publisher: National Academies Press
Release Date : 2019-08-22
Robust Machine Learning Algorithms And Systems For Detection And Mitigation Of Adversarial Attacks And Anomalies written by National Academies of Sciences, Engineering, and Medicine and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-22 with Computers categories.
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.
Deep Learning Applications For Cyber Security
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Author : Mamoun Alazab
language : en
Publisher: Springer
Release Date : 2019-08-14
Deep Learning Applications For Cyber Security written by Mamoun Alazab and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-14 with Computers categories.
Cybercrime remains a growing challenge in terms of security and privacy practices. Working together, deep learning and cyber security experts have recently made significant advances in the fields of intrusion detection, malicious code analysis and forensic identification. This book addresses questions of how deep learning methods can be used to advance cyber security objectives, including detection, modeling, monitoring and analysis of as well as defense against various threats to sensitive data and security systems. Filling an important gap between deep learning and cyber security communities, it discusses topics covering a wide range of modern and practical deep learning techniques, frameworks and development tools to enable readers to engage with the cutting-edge research across various aspects of cyber security. The book focuses on mature and proven techniques, and provides ample examples to help readers grasp the key points.