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Machine Learning Security Principles

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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.

Machine Learning For Computer And Cyber Security

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Author : Brij B. Gupta
language : en
Publisher: CRC Press
Release Date : 2019-02-05

Machine Learning For Computer And Cyber Security written by Brij B. Gupta and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-05 with Computers categories.

While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning is a branch of Computer Science which enables computing machines to adopt new behaviors on the basis of observable and verifiable data and information. It can be applied to ensure the security of the computers and the information by detecting anomalies using data mining and other such techniques. This book will be an invaluable resource to understand the importance of machine learning and data mining in establishing computer and cyber security. It emphasizes important security aspects associated with computer and cyber security along with the analysis of machine learning and data mining based solutions. The book also highlights the future research domains in which these solutions can be applied. Furthermore, it caters to the needs of IT professionals, researchers, faculty members, scientists, graduate students, research scholars and software developers who seek to carry out research and develop combating solutions in the area of cyber security using machine learning based approaches. It is an extensive source of information for the readers belonging to the field of Computer Science and Engineering, and Cyber Security professionals. Key Features: This book contains examples and illustrations to demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security. It showcases important security aspects and current trends in the field. It provides an insight of the future research directions in the field. Contents of this book help to prepare the students for exercising better defense in terms of understanding the motivation of the attackers and how to deal with and mitigate the situation using machine learning based approaches in better manner.

Machine Learning For Computer And Cyber Security

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Author : Brij B. Gupta
language : en
Publisher: CRC Press
Release Date : 2019-02-05

10 Machine Learning Blueprints You Should Know For Cybersecurity

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Author : Rajvardhan Oak
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-05-31

10 Machine Learning Blueprints You Should Know For Cybersecurity written by Rajvardhan Oak 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 2023-05-31 with Computers categories.

Work on 10 practical projects, each with a blueprint for a different machine learning technique, and apply them in the real world to fight against cybercrime Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn how to frame a cyber security problem as a machine learning problem Examine your model for robustness against adversarial machine learning Build your portfolio, enhance your resume, and ace interviews to become a cybersecurity data scientist Book Description Machine learning in security is harder than other domains because of the changing nature and abilities of adversaries, high stakes, and a lack of ground-truth data. This book will prepare machine learning practitioners to effectively handle tasks in the challenging yet exciting cybersecurity space. The book begins by helping you understand how advanced ML algorithms work and shows you practical examples of how they can be applied to security-specific problems with Python – by using open source datasets or instructing you to create your own. In one exercise, you'll also use GPT 3.5, the secret sauce behind ChatGPT, to generate an artificial dataset of fabricated news. Later, you'll find out how to apply the expert knowledge and human-in-the-loop decision-making that is necessary in the cybersecurity space. This book is designed to address the lack of proper resources available for individuals interested in transitioning into a data scientist role in cybersecurity. It concludes with case studies, interview questions, and blueprints for four projects that you can use to enhance your portfolio. By the end of this book, you'll be able to apply machine learning algorithms to detect malware, fake news, deep fakes, and more, along with implementing privacy-preserving machine learning techniques such as differentially private ML. What you will learn Use GNNs to build feature-rich graphs for bot detection and engineer graph-powered embeddings and features Discover how to apply ML techniques in the cybersecurity domain Apply state-of-the-art algorithms such as transformers and GNNs to solve security-related issues Leverage ML to solve modern security issues such as deep fake detection, machine-generated text identification, and stylometric analysis Apply privacy-preserving ML techniques and use differential privacy to protect user data while training ML models Build your own portfolio with end-to-end ML projects for cybersecurity Who this book is for This book is for machine learning practitioners interested in applying their skills to solve cybersecurity issues. Cybersecurity workers looking to leverage ML methods will also find this book useful. An understanding of the fundamental machine learning concepts and beginner-level knowledge of Python programming are needed to grasp the concepts in this book. Whether you're a beginner or an experienced professional, this book offers a unique and valuable learning experience that'll help you develop the skills needed to protect your network and data against the ever-evolving threat landscape.

Machine Learning Security With Azure

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Author : Georgia Kalyva
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-12-28

Machine Learning Security With Azure written by Georgia Kalyva 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 2023-12-28 with Computers categories.

Implement industry best practices to identify vulnerabilities and protect your data, models, environment, and applications while learning how to recover from a security breach Key Features Learn about machine learning attacks and assess your workloads for vulnerabilities Gain insights into securing data, infrastructure, and workloads effectively Discover how to set and maintain a better security posture with the Azure Machine Learning platform Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWith AI and machine learning (ML) models gaining popularity and integrating into more and more applications, it is more important than ever to ensure that models perform accurately and are not vulnerable to cyberattacks. However, attacks can target your data or environment as well. This book will help you identify security risks and apply the best practices to protect your assets on multiple levels, from data and models to applications and infrastructure. This book begins by introducing what some common ML attacks are, how to identify your risks, and the industry standards and responsible AI principles you need to follow to gain an understanding of what you need to protect. Next, you will learn about the best practices to secure your assets. Starting with data protection and governance and then moving on to protect your infrastructure, you will gain insights into managing and securing your Azure ML workspace. This book introduces DevOps practices to automate your tasks securely and explains how to recover from ML attacks. Finally, you will learn how to set a security benchmark for your scenario and best practices to maintain and monitor your security posture. By the end of this book, you’ll be able to implement best practices to assess and secure your ML assets throughout the Azure Machine Learning life cycle.What you will learn Explore the Azure Machine Learning project life cycle and services Assess the vulnerability of your ML assets using the Zero Trust model Explore essential controls to ensure data governance and compliance in Azure Understand different methods to secure your data, models, and infrastructure against attacks Find out how to detect and remediate past or ongoing attacks Explore methods to recover from a security breach Monitor and maintain your security posture with the right tools and best practices Who this book is for This book is for anyone looking to learn how to assess, secure, and monitor every aspect of AI or machine learning projects running on the Microsoft Azure platform using the latest security and compliance, industry best practices, and standards. This is a must-have resource for machine learning developers and data scientists working on ML projects. IT administrators, DevOps, and security engineers required to secure and monitor Azure workloads will also benefit from this book, as the chapters cover everything from implementation to deployment, AI attack prevention, and recovery.

Owasp Security Principles And Practices

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-17

Owasp Security Principles And Practices written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-17 with Computers categories.

"OWASP Security Principles and Practices" "OWASP Security Principles and Practices" is an authoritative guidebook designed for modern security professionals, architects, and software engineers who seek to build resilient, high-assurance applications in an ever-evolving threat landscape. Rooted in OWASP’s globally recognized mission and standards, this book offers a comprehensive exploration of foundational security frameworks, methodologies such as threat modeling, and the seamless integration of secure practices into contemporary Agile, DevOps, and cloud-native environments. Through detailed analysis of the OWASP Top Ten, ASVS, and proactive controls, readers gain a deep understanding of the industry’s most impactful projects and community-driven standards. Each chapter progressively delves into critical pillars of application security, covering secure design and architecture, robust authentication and authorization strategies, and sophisticated techniques for data protection and regulatory compliance. Essential topics such as the prevention of injection and input-related attacks, advanced security testing automation, and secure code review are systematically unpacked, equipping readers with actionable guidance for both process improvement and hands-on defense. In-depth treatments of supply chain security, operational hardening, and incident response ensure a holistic perspective that empowers organizations to build, deploy, and maintain secure applications at scale. With special attention to emerging challenges—including API and AI security, privacy-enhancing technologies, quantum-ready cryptography, and security automation—this book not only addresses present-day risks but also prepares readers for the next generation of threats and opportunities. Enriched by step-by-step guides, real-world scenarios, and insights from OWASP’s global community, "OWASP Security Principles and Practices" stands as an essential resource for anyone committed to advancing the state of application security and fostering a culture of continuous resilience.

Agentic Assurance Identity First Devops Ai Security And Digital Transformation In Insurance Finance

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Author : PALLAV KUMAR KAULWAR
language : en
Publisher: AQUA PUBLICATIONS
Release Date :

Agentic Assurance Identity First Devops Ai Security And Digital Transformation In Insurance Finance written by PALLAV KUMAR KAULWAR and has been published by AQUA PUBLICATIONS this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

Deep Learning Innovations For Securing Critical Infrastructures

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Author : Kumar, Rajeev
language : en
Publisher: IGI Global
Release Date : 2025-04-18

Deep Learning Innovations For Securing Critical Infrastructures written by Kumar, Rajeev and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-18 with Computers categories.

Deep learning innovations play a crucial role in securing critical infrastructures, offering advanced solutions to protect vital systems from sophisticated cyber threats. By leveraging neural networks and advanced algorithms, deep learning enables real-time anomaly detection, pattern recognition, and predictive threat analysis, which are essential for safeguarding critical sectors such as energy, transportation, healthcare, and finance. These technologies can identify vulnerabilities, respond to breaches, and adapt to new attacks, providing a strong defense against cyber risks. As the digital landscape becomes more interconnected, the integration of deep learning into cybersecurity strategies will enhance resilience while ensuring the safe operation of essential services. Deep Learning Innovations for Securing Critical Infrastructures explores the cutting-edge integration of neural networks and artificial intelligence (AI) in modern cybersecurity systems. It examines how AI, particularly neural network models, is revolutionizing cybersecurity by automating threat detection, analyzing complex data patterns, and implementing proactive defense mechanisms. This book covers topics such as blockchain, cloud computing, and event management, and is a useful resource for business owners, computer engineers, data scientists, academicians, and researchers.

Electronics Communications And Networks

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Author : Antonio J. Tallón-Ballesteros
language : en
Publisher: IOS Press
Release Date : 2024-01-15

Electronics Communications And Networks written by Antonio J. Tallón-Ballesteros and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-15 with Computers categories.

It is hard to imagine a world without electronic communication networks, so dependent have we all become on the networks which now exist and have become part of the fabric of our daily lives. This book presents papers from CECNet 2023, the 13th International Conference on Electronics, Communications and Networks, held as a hybrid event, in person in Macau, China and online via Microsoft Teams, from 17-20 November 2023. This annual conference provides a comprehensive, global forum for experts and participants from academia to exchange ideas and present the results of ongoing research in state-of-the-art areas of electronics technology, communications engineering and technology, wireless communications engineering and technology, and computer engineering and technology. A total of 324 submissions were received for the conference, and those which qualified by virtue of falling under the scope of the conference topics were exhaustively reviewed by program committee members and peer-reviewers, taking into account the breadth and depth of the relevant research topics. The 101 selected contributions included in this book present innovative, original ideas or results of general significance, supported by clear and rigorous reasoning and compelling new light in both evidence and method. Subjects covered divide broadly into 3 categories: electronics technology and VLSI, internet technology and signal processing, and information communication and communication networks. Providing an overview of current research and developments in these rapidly evolving fields, the book will be of interest to all those working with digital communications networks.

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.

Machine Learning Security Principles

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