Download or read book Attacks Defenses and Testing for Deep Learning written by Jinyin Chen and published by Springer Nature. This book was released on with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 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 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 published by National Academies Press. This book was released on 2019-08-22 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Machine Learning for Cyber Security written by Xiaofeng Chen and published by Springer Nature. This book was released on 2020-11-10 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi’an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.

Download or read book A Machine Learning Approach to Phishing Detection and Defense written by Iraj Sadegh Amiri and published by Syngress. This book was released on 2014-12-05 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Phishing is one of the most widely-perpetrated forms of cyber attack, used to gather sensitive information such as credit card numbers, bank account numbers, and user logins and passwords, as well as other information entered via a web site. The authors of A Machine-Learning Approach to Phishing Detetion and Defense have conducted research to demonstrate how a machine learning algorithm can be used as an effective and efficient tool in detecting phishing websites and designating them as information security threats. This methodology can prove useful to a wide variety of businesses and organizations who are seeking solutions to this long-standing threat. A Machine-Learning Approach to Phishing Detetion and Defense also provides information security researchers with a starting point for leveraging the machine algorithm approach as a solution to other information security threats. Discover novel research into the uses of machine-learning principles and algorithms to detect and prevent phishing attacks Help your business or organization avoid costly damage from phishing sources Gain insight into machine-learning strategies for facing a variety of information security threats

Download or read book Adversarial Learning and Secure AI written by David J. Miller and published by Cambridge University Press. This book was released on 2023-08-31 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Machine Learning for Cyber Security written by Yuan Xu and published by Springer Nature. This book was released on 2023-01-12 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume proceedings set LNCS 13655,13656 and 13657 constitutes the refereedproceedings of the 4th International Conference on Machine Learning for Cyber Security, ML4CS 2022, which taking place during December 2–4, 2022, held in Guangzhou, China. The 100 full papers and 46 short papers were included in these proceedings were carefully reviewed and selected from 367 submissions.

Download or read book Deep Learning Applications for Cyber Security written by Mamoun Alazab and published by Springer. This book was released on 2019-08-14 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computational Intelligence for Clinical Diagnosis written by Ferdin Joe John Joseph and published by Springer Nature. This book was released on 2023-06-05 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains multidisciplinary advancements in healthcare and technology through artificial intelligence (AI). The topics are crafted in such a way to cover all the areas of healthcare that require AI for further development. Some of the topics that contain algorithms and techniques are explained with the help of source code developed by the chapter contributors. The book covers the advancements in AI and healthcare from the Covid 19 pandemic and also analyzes the readiness and need for advancements in managing yet another pandemic in the future. Most of the technologies addressed in this book are added with a concept of encapsulation to obtain a cookbook for anyone who needs to reskill or upskill themselves in order to contribute to an advancement in the field. This book benefits students, professionals, and anyone from any background to learn about digital disruptions in healthcare.

Download or read book Deployable Machine Learning for Security Defense written by Gang Wang and published by Springer Nature. This book was released on 2020-10-17 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes selected papers from the First International Workshop on Deployable Machine Learning for Security Defense, MLHat 2020, held in August 2020. Due to the COVID-19 pandemic the conference was held online. The 8 full papers were thoroughly reviewed and selected from 13 qualified submissions. The papers are organized in the following topical sections: understanding the adversaries; adversarial ML for better security; threats on networks.

Download or read book The International Conference on Deep Learning Big Data and Blockchain DBB 2022 written by Irfan Awan and published by Springer Nature. This book was released on 2022-08-31 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep and machine learning is the state-of-the-art at providing models, methods, tools and techniques for developing autonomous and intelligent systems which can revolutionise industrial and commercial applications in various fields such as online commerce, intelligent transportation, healthcare and medicine, etc. The ground-breaking technology of blockchain also enables decentralisation, immutability, and transparency of data and applications. This event aims to enable synergy between these areas and provide a leading forum for researchers, developers, practitioners, and professionals from public sectors and industries to meet and share the latest solutions and ideas in solving cutting-edge problems in the modern information society and the economy. The conference focuses on specific challenges in deep (and machine) learning, big data and blockchain. Some of the key topics of interest include (but are not limited to): Deep/Machine learning based models Statistical models and learning Data analysis, insights and hidden pattern Data visualisation Security threat detection Data classification and clustering Blockchain security and trust Blockchain data management

Download or read book Strengthening Deep Neural Networks written by Katy Warr and published by "O'Reilly Media, Inc.". This book was released on 2019-07-03 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Defense Against Test time Evasion Attacks and Backdoor Attacks written by Hang Wang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Game Theory and Machine Learning for Cyber Security written by Charles A. Kamhoua and published by John Wiley & Sons. This book was released on 2021-09-08 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. Readers will also enjoy: A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deception An exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threats Practical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systems In-depth examinations of generative models for cyber security Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security.

Download or read book Mastering Machine Learning for Penetration Testing written by Chiheb Chebbi and published by Packt Publishing Ltd. This book was released on 2018-06-27 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book Description Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is for This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.

Download or read book Machine Learning and Knowledge Discovery in Databases written by Peggy Cellier and published by Springer Nature. This book was released on 2020-03-27 with total page 755 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019.