Introduction To Machine Learning With Security
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Introduction To Machine Learning With Security
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Author : Pramod Gupta
language : en
Publisher: Springer Nature
Release Date : 2024-07-12
Introduction To Machine Learning With Security written by Pramod Gupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-12 with Technology & Engineering categories.
This book provides an introduction to machine learning, security and cloud computing, from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select and make use of appropriate techniques. Important topics are demonstrated using real applications and case studies.
Introduction To Machine Learning With Applications In Information Security
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Author : Mark Stamp
language : en
Publisher: CRC Press
Release Date : 2017-09-22
Introduction To Machine Learning With Applications In Information Security written by Mark Stamp and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-22 with Business & Economics categories.
Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove theorems, or otherwise dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core machine learning topics in-depth, including Hidden Markov Models, Principal Component Analysis, Support Vector Machines, and Clustering. It also includes coverage of Nearest Neighbors, Neural Networks, Boosting and AdaBoost, Random Forests, Linear Discriminant Analysis, Vector Quantization, Naive Bayes, Regression Analysis, Conditional Random Fields, and Data Analysis. Most of the examples in the book are drawn from the field of information security, with many of the machine learning applications specifically focused on malware. The applications presented are designed to demystify machine learning techniques by providing straightforward scenarios. Many of the exercises in this book require some programming, and basic computing concepts are assumed in a few of the application sections. However, anyone with a modest amount of programming experience should have no trouble with this aspect of the book. Instructor resources, including PowerPoint slides, lecture videos, and other relevant material are provided on an accompanying website: http://www.cs.sjsu.edu/~stamp/ML/. For the reader’s benefit, the figures in the book are also available in electronic form, and in color. About the Author Mark Stamp has been a Professor of Computer Science at San Jose State University since 2002. Prior to that, he worked at the National Security Agency (NSA) for seven years, and a Silicon Valley startup company for two years. He received his Ph.D. from Texas Tech University in 1992. His love affair with machine learning began in the early 1990s, when he was working at the NSA, and continues today at SJSU, where he has supervised vast numbers of master’s student projects, most of which involve a combination of information security and machine learning.
Data Science And Security
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Author : Samiksha Shukla
language : en
Publisher: Springer Nature
Release Date : 2022-07-01
Data Science And Security written by Samiksha Shukla and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-01 with Technology & Engineering categories.
This book presents best selected papers presented at the International Conference on Data Science for Computational Security (IDSCS 2022), organized by the Department of Data Science, CHRIST (Deemed to be University), Pune Lavasa Campus, India, during 11 – 12 February 2022. The book proposes new technologies and discusses future solutions and applications of data science, data analytics and security. The book targets current research works in the areas of data science, data security, data analytics, artificial intelligence, machine learning, computer vision, algorithms design, computer networking, data mining, big data, text mining, knowledge representation, soft computing and cloud computing.
Machine Learning Security Principles
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Author : John Paul Mueller
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-12-30
Machine Learning Security Principles written by John Paul Mueller and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-30 with Computers categories.
Thwart hackers by preventing, detecting, and misdirecting access before they can plant malware, obtain credentials, engage in fraud, modify data, poison models, corrupt users, eavesdrop, and otherwise ruin your day Key Features Discover how hackers rely on misdirection and deep fakes to fool even the best security systems Retain the usefulness of your data by detecting unwanted and invalid modifications Develop application code to meet the security requirements related to machine learning Book DescriptionBusinesses are leveraging the power of AI to make undertakings that used to be complicated and pricy much easier, faster, and cheaper. The first part of this book will explore these processes in more depth, which will help you in understanding the role security plays in machine learning. As you progress to the second part, you’ll learn more about the environments where ML is commonly used and dive into the security threats that plague them using code, graphics, and real-world references. The next part of the book will guide you through the process of detecting hacker behaviors in the modern computing environment, where fraud takes many forms in ML, from gaining sales through fake reviews to destroying an adversary’s reputation. Once you’ve understood hacker goals and detection techniques, you’ll learn about the ramifications of deep fakes, followed by mitigation strategies. This book also takes you through best practices for embracing ethical data sourcing, which reduces the security risk associated with data. You’ll see how the simple act of removing personally identifiable information (PII) from a dataset lowers the risk of social engineering attacks. By the end of this machine learning book, you'll have an increased awareness of the various attacks and the techniques to secure your ML systems effectively.What you will learn Explore methods to detect and prevent illegal access to your system Implement detection techniques when access does occur Employ machine learning techniques to determine motivations Mitigate hacker access once security is breached Perform statistical measurement and behavior analysis Repair damage to your data and applications Use ethical data collection methods to reduce security risks Who this book is forWhether you’re a data scientist, researcher, or manager working with machine learning techniques in any aspect, this security book is a must-have. While most resources available on this topic are written in a language more suitable for experts, this guide presents security in an easy-to-understand way, employing a host of diagrams to explain concepts to visual learners. While familiarity with machine learning concepts is assumed, knowledge of Python and programming in general will be useful.
Game Theory And Machine Learning For Cyber Security
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Author : Charles A. Kamhoua
language : en
Publisher: John Wiley & Sons
Release Date : 2021-09-15
Game Theory And Machine Learning For Cyber Security written by Charles A. Kamhoua 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 2021-09-15 with Technology & Engineering categories.
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.
Machine Learning And Security
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Author : Clarence Chio
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-01-26
Machine Learning And Security written by Clarence Chio and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-26 with Computers categories.
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions
Guide To Vulnerability Analysis For Computer Networks And Systems
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Author : Simon Parkinson
language : en
Publisher: Springer
Release Date : 2018-09-04
Guide To Vulnerability Analysis For Computer Networks And Systems written by Simon Parkinson and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-04 with Computers categories.
This professional guide and reference examines the challenges of assessing security vulnerabilities in computing infrastructure. Various aspects of vulnerability assessment are covered in detail, including recent advancements in reducing the requirement for expert knowledge through novel applications of artificial intelligence. The work also offers a series of case studies on how to develop and perform vulnerability assessment techniques using start-of-the-art intelligent mechanisms. Topics and features: provides tutorial activities and thought-provoking questions in each chapter, together with numerous case studies; introduces the fundamentals of vulnerability assessment, and reviews the state of the art of research in this area; discusses vulnerability assessment frameworks, including frameworks for industrial control and cloud systems; examines a range of applications that make use of artificial intelligence to enhance the vulnerability assessment processes; presents visualisation techniques that can be used to assist the vulnerability assessment process. In addition to serving the needs of security practitioners and researchers, this accessible volume is also ideal for students and instructors seeking a primer on artificial intelligence for vulnerability assessment, or a supplementary text for courses on computer security, networking, and artificial intelligence.
Computational Science And Its Applications Iccsa 2021
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Author : Osvaldo Gervasi
language : en
Publisher: Springer Nature
Release Date : 2021-09-10
Computational Science And Its Applications Iccsa 2021 written by Osvaldo Gervasi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-10 with Computers categories.
The ten-volume set LNCS 12949 – 12958 constitutes the proceedings of the 21st International Conference on Computational Science and Its Applications, ICCSA 2021, which was held in Cagliari, Italy, during September 13 – 16, 2021. The event was organized in a hybrid mode due to the Covid-19 pandemic.The 466 full and 18 short papers presented in these proceedings were carefully reviewed and selected from 1588 submissions. The books cover such topics as multicore architectures, mobile and wireless security, sensor networks, open source software, collaborative and social computing systems and tools, cryptography, human computer interaction, software design engineering, and others. Part III of the set icludes papers on Information Systems and Technologies and the proceeding of the following workshops: International Workshop on Automatic landform classification: spatial methods and applications (ALCSMA 2021); International Workshop on Application of Numerical Analysis to Imaging Science (ANAIS 2021); International Workshop on Advances in information Systems and Technologies for Emergency management, risk assessment and mitigationbased on the Resilience concepts (ASTER 2021); International Workshop on Advances in Web Based Learning (AWBL 2021).
Network And System Security
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Author : Xingliang Yuan
language : en
Publisher: Springer Nature
Release Date : 2022-12-06
Network And System Security written by Xingliang Yuan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-06 with Computers categories.
This book constitutes the refereed proceedings of the 16th International Conference on Network and System Security, NSS 2022, held in Denarau Island, Fiji, on December 9-12, 2022. The 23 full and 18 short papers presented in this book were carefully reviewed and selected from 83 submissions. They focus on theoretical and practical aspects of network and system security, such as authentication, access control, availability, integrity, privacy, confidentiality, dependability and sustainability of computer networks and systems.
Quantum Resistant Artificial Intelligence And Machine Learning Architectures For Secure Mortgage And Banking Intelligence Systems
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Author : Dimple Ravindra Patil
language : en
Publisher: Deep Science Publishing
Release Date : 2024-11-20
Quantum Resistant Artificial Intelligence And Machine Learning Architectures For Secure Mortgage And Banking Intelligence Systems written by Dimple Ravindra Patil and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-20 with Computers categories.
Along with the development of artificial intelligence and financial technologies, the fast convergence of quantum computing is one of the most important technological trends of the twenty-first century. Though artificial intelligence and machine learning have already revolutionized the mortgage and banking intelligence systems- improving credit risk evaluation, fraud level detection, compliance automation and decision-making efficiency purposes, the coming up of large-scale quantum computing is a deep disruptive force of cryptographic principles on which these systems operate. Classical security models securing the financial data over several decades are becoming susceptible to quantum-enabled threats, which is why quantum-resistant architectures providing long-term confidentiality, integrity, and trust are urgently needed. It is on this critical inflection point that this book was driven by the fact that innovation has to be coupled by foresight, strength and responsible system design. Quantum-Resistant Artificial Intelligence and Machine Learning Architectures of Secure Mortgage and Banking Intelligence Systems is an interdisciplinary and detailed analysis of the manner in which financial AI systems can be kept secure in the post-quantum age. The book combines the most recent findings in quantum threat management, post-quantum cryptography, federated learning, secure training of a model, hybrid authentication, adversarial resilience, explainable AI, and quantum-safe security control performance implications. All the chapters discuss in their own systematic fashion application, techniques, methodologies, challenges, opportunities, impacts, and the future trend of research with a special love given to the mortgage and banking ecosystems where data longevity, regulatory compliance, and systemic stability are the key consideration. The book unites insights in the field of cryptography, machine learning, financial engineering, and governance by shifting the focus of the concept of algorithmic substitution to a broader perspective of security as a system-wide and lifecycle-oriented problem. The book should be read by researchers, graduate students, practitioners in the industry, and policymakers as well as regulators who are intersectional in artificial intelligence, cybersecurity, and financial services. It will also be used as a reference point to gain an overview of the impact of quantum risks in financial AI systems, as well as as a practical guide to architectural design, evaluation and transition to quantum-resilient systems. Since risky decision-making is becoming more and more reliant on automated intelligence by financial institutions, even passive quantum preparedness is no longer a choice, but rather the key to continuing to trust, maintain compliance and prevent a financial meltdown in the global marketplace. We do hope that this book will lead to additional research, co-operation and judicious action on constructing safe, open, and robust financial intelligence systems of the quantum age.