Attacks, Defenses and Testing for Deep Learning
Author : Jinyin Chen
Publisher : Springer Nature
Page : 413 pages
File Size : 40,56 MB
Release :
Category :
ISBN : 9819704251
Machine Learning for Cyber Security
Author : Yuan Xu
Publisher : Springer Nature
Page : 707 pages
File Size : 30,79 MB
Release : 2023-01-12
Category : Computers
ISBN : 3031201027
Book Description
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.
Computational Intelligence for Clinical Diagnosis
Author : Ferdin Joe John Joseph
Publisher : Springer Nature
Page : 584 pages
File Size : 17,91 MB
Release : 2023-06-05
Category : Technology & Engineering
ISBN : 3031236831
Book Description
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.
The International Conference on Deep Learning, Big Data and Blockchain (DBB 2022)
Author : Irfan Awan
Publisher : Springer Nature
Page : 140 pages
File Size : 26,79 MB
Release : 2022-08-31
Category : Technology & Engineering
ISBN : 3031160355
Book Description
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
Strengthening Deep Neural Networks
Author : Katy Warr
Publisher : "O'Reilly Media, Inc."
Page : 233 pages
File Size : 35,45 MB
Release : 2019-07-03
Category : Computers
ISBN : 1492044903
Book Description
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
Game Theory and Machine Learning for Cyber Security
Author : Charles A. Kamhoua
Publisher : John Wiley & Sons
Page : 546 pages
File Size : 50,79 MB
Release : 2021-09-08
Category : Technology & Engineering
ISBN : 1119723949
Book Description
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.
Mastering Machine Learning for Penetration Testing
Author : Chiheb Chebbi
Publisher : Packt Publishing Ltd
Page : 264 pages
File Size : 44,86 MB
Release : 2018-06-27
Category : Language Arts & Disciplines
ISBN : 178899311X
Book Description
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.
Deep Learning Applications for Cyber Security
Author : Mamoun Alazab
Publisher : Springer
Page : 260 pages
File Size : 41,35 MB
Release : 2019-08-14
Category : Computers
ISBN : 3030130576
Book Description
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.
Adversarial Machine Learning
Author : Aneesh Sreevallabh Chivukula
Publisher : Springer Nature
Page : 316 pages
File Size : 50,38 MB
Release : 2023-03-06
Category : Computers
ISBN : 3030997723
Book Description
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.
Adversarial Learning and Secure AI
Author : David J. Miller
Publisher : Cambridge University Press
Page : 376 pages
File Size : 27,20 MB
Release : 2023-08-31
Category : Computers
ISBN : 100931565X
Book Description
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.
