10 Machine Learning Blueprints You Should Know For Cybersecurity

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10 Machine Learning Blueprints You Should Know for Cybersecurity

Rajvardhan Oak
10 Machine Learning Blueprints You Should Know for Cybersecurity Book PDF

Author : Rajvardhan Oak
Publisher : Packt Publishing Ltd
Page : 330 pages
File Size : 44,7 Mb
Release : 2023-05-31
Category : Computers
ISBN : 9781804611975

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10 Machine Learning Blueprints You Should Know for Cybersecurity by Rajvardhan Oak Pdf

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.

10 Machine Learning Blueprints You Should Know for Cybersecurity

Rajvardhan Oak
10 Machine Learning Blueprints You Should Know for Cybersecurity Book PDF

Author : Rajvardhan Oak
Publisher : Unknown
Page : 0 pages
File Size : 44,6 Mb
Release : 2023-05-31
Category : Electronic
ISBN : 1804619477

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10 Machine Learning Blueprints You Should Know for Cybersecurity by Rajvardhan Oak Pdf

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.

Hands-On Machine Learning for Cybersecurity

Soma Halder,Sinan Ozdemir
Hands On Machine Learning for Cybersecurity Book PDF

Author : Soma Halder,Sinan Ozdemir
Publisher : Packt Publishing Ltd
Page : 306 pages
File Size : 42,7 Mb
Release : 2018-12-31
Category : Computers
ISBN : 9781788990967

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Hands-On Machine Learning for Cybersecurity by Soma Halder,Sinan Ozdemir Pdf

Get into the world of smart data security using machine learning algorithms and Python libraries Key FeaturesLearn machine learning algorithms and cybersecurity fundamentalsAutomate your daily workflow by applying use cases to many facets of securityImplement smart machine learning solutions to detect various cybersecurity problemsBook Description Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems What you will learnUse machine learning algorithms with complex datasets to implement cybersecurity conceptsImplement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problemsLearn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDAUnderstand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimesUse TensorFlow in the cybersecurity domain and implement real-world examplesLearn how machine learning and Python can be used in complex cyber issuesWho this book is for This book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book

Artificial Intelligence for Cybersecurity

Mark Stamp,Corrado Aaron Visaggio,Francesco Mercaldo,Fabio Di Troia
Artificial Intelligence for Cybersecurity Book PDF

Author : Mark Stamp,Corrado Aaron Visaggio,Francesco Mercaldo,Fabio Di Troia
Publisher : Springer Nature
Page : 388 pages
File Size : 41,6 Mb
Release : 2022-07-15
Category : Computers
ISBN : 9783030970871

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Artificial Intelligence for Cybersecurity by Mark Stamp,Corrado Aaron Visaggio,Francesco Mercaldo,Fabio Di Troia Pdf

This book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniques to datasets and instead delves into deeper issues that arise at the interface between deep learning and cybersecurity. This book also provides insight into the difficult "how" and "why" questions that arise in AI within the security domain. For example, this book includes chapters covering "explainable AI", "adversarial learning", "resilient AI", and a wide variety of related topics. It’s not limited to any specific cybersecurity subtopics and the chapters touch upon a wide range of cybersecurity domains, ranging from malware to biometrics and more. Researchers and advanced level students working and studying in the fields of cybersecurity (equivalently, information security) or artificial intelligence (including deep learning, machine learning, big data, and related fields) will want to purchase this book as a reference. Practitioners working within these fields will also be interested in purchasing this book.

Machine Learning for Cybersecurity Cookbook

Emmanuel Tsukerman
Machine Learning for Cybersecurity Cookbook Book PDF

Author : Emmanuel Tsukerman
Publisher : Packt Publishing Ltd
Page : 338 pages
File Size : 47,9 Mb
Release : 2019-11-25
Category : Computers
ISBN : 9781838556341

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Machine Learning for Cybersecurity Cookbook by Emmanuel Tsukerman Pdf

Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key FeaturesManage data of varying complexity to protect your system using the Python ecosystemApply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineeringAutomate your daily workflow by addressing various security challenges using the recipes covered in the bookBook Description Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach. What you will learnLearn how to build malware classifiers to detect suspicious activitiesApply ML to generate custom malware to pentest your securityUse ML algorithms with complex datasets to implement cybersecurity conceptsCreate neural networks to identify fake videos and imagesSecure your organization from one of the most popular threats – insider threatsDefend against zero-day threats by constructing an anomaly detection systemDetect web vulnerabilities effectively by combining Metasploit and MLUnderstand how to train a model without exposing the training dataWho this book is for This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book.

Hands-On Artificial Intelligence for Cybersecurity

Alessandro Parisi
Hands On Artificial Intelligence for Cybersecurity Book PDF

Author : Alessandro Parisi
Publisher : Packt Publishing Ltd
Page : 331 pages
File Size : 44,5 Mb
Release : 2019-08-02
Category : Computers
ISBN : 9781789805178

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Hands-On Artificial Intelligence for Cybersecurity by Alessandro Parisi Pdf

Build smart cybersecurity systems with the power of machine learning and deep learning to protect your corporate assets Key FeaturesIdentify and predict security threats using artificial intelligenceDevelop intelligent systems that can detect unusual and suspicious patterns and attacksLearn how to test the effectiveness of your AI cybersecurity algorithms and toolsBook Description Today's organizations spend billions of dollars globally on cybersecurity. Artificial intelligence has emerged as a great solution for building smarter and safer security systems that allow you to predict and detect suspicious network activity, such as phishing or unauthorized intrusions. This cybersecurity book presents and demonstrates popular and successful AI approaches and models that you can adapt to detect potential attacks and protect your corporate systems. You'll learn about the role of machine learning and neural networks, as well as deep learning in cybersecurity, and you'll also learn how you can infuse AI capabilities into building smart defensive mechanisms. As you advance, you'll be able to apply these strategies across a variety of applications, including spam filters, network intrusion detection, botnet detection, and secure authentication. By the end of this book, you'll be ready to develop intelligent systems that can detect unusual and suspicious patterns and attacks, thereby developing strong network security defenses using AI. What you will learnDetect email threats such as spamming and phishing using AICategorize APT, zero-days, and polymorphic malware samplesOvercome antivirus limits in threat detectionPredict network intrusions and detect anomalies with machine learningVerify the strength of biometric authentication procedures with deep learningEvaluate cybersecurity strategies and learn how you can improve themWho this book is for If you’re a cybersecurity professional or ethical hacker who wants to build intelligent systems using the power of machine learning and AI, you’ll find this book useful. Familiarity with cybersecurity concepts and knowledge of Python programming is essential to get the most out of this book.

Cyber Security Meets Machine Learning

Xiaofeng Chen,Willy Susilo,Elisa Bertino
Cyber Security Meets Machine Learning Book PDF

Author : Xiaofeng Chen,Willy Susilo,Elisa Bertino
Publisher : Springer Nature
Page : 168 pages
File Size : 42,7 Mb
Release : 2021-07-02
Category : Computers
ISBN : 9789813367265

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Cyber Security Meets Machine Learning by Xiaofeng Chen,Willy Susilo,Elisa Bertino Pdf

Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

Machine Learning for Cyber Agents

Stanislav Abaimov,Maurizio Martellini
Machine Learning for Cyber Agents Book PDF

Author : Stanislav Abaimov,Maurizio Martellini
Publisher : Springer Nature
Page : 235 pages
File Size : 40,8 Mb
Release : 2022-01-27
Category : Computers
ISBN : 9783030915858

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Machine Learning for Cyber Agents by Stanislav Abaimov,Maurizio Martellini Pdf

The cyber world has been both enhanced and endangered by AI. On the one hand, the performance of many existing security services has been improved, and new tools created. On the other, it entails new cyber threats both through evolved attacking capacities and through its own imperfections and vulnerabilities. Moreover, quantum computers are further pushing the boundaries of what is possible, by making machine learning cyber agents faster and smarter. With the abundance of often-confusing information and lack of trust in the diverse applications of AI-based technologies, it is essential to have a book that can explain, from a cyber security standpoint, why and at what stage the emerging, powerful technology of machine learning can and should be mistrusted, and how to benefit from it while avoiding potentially disastrous consequences. In addition, this book sheds light on another highly sensitive area – the application of machine learning for offensive purposes, an aspect that is widely misunderstood, under-represented in the academic literature and requires immediate expert attention.

Machine Learning for Cyber Security

Preeti Malik,Lata Nautiyal,Mangey Ram
Machine Learning for Cyber Security Book PDF

Author : Preeti Malik,Lata Nautiyal,Mangey Ram
Publisher : Walter de Gruyter GmbH & Co KG
Page : 170 pages
File Size : 51,6 Mb
Release : 2022-12-05
Category : Business & Economics
ISBN : 9783110766769

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Machine Learning for Cyber Security by Preeti Malik,Lata Nautiyal,Mangey Ram Pdf

This book shows how machine learning (ML) methods can be used to enhance cyber security operations, including detection, modeling, monitoring as well as defense against threats to sensitive data and security systems. Filling an important gap between ML and cyber security communities, it discusses topics covering a wide range of modern and practical ML techniques, frameworks and tools.

Adversary-Aware Learning Techniques and Trends in Cybersecurity

Prithviraj Dasgupta,Joseph B. Collins,Ranjeev Mittu
Adversary Aware Learning Techniques and Trends in Cybersecurity Book PDF

Author : Prithviraj Dasgupta,Joseph B. Collins,Ranjeev Mittu
Publisher : Springer Nature
Page : 229 pages
File Size : 49,5 Mb
Release : 2021-01-22
Category : Computers
ISBN : 9783030556921

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Adversary-Aware Learning Techniques and Trends in Cybersecurity by Prithviraj Dasgupta,Joseph B. Collins,Ranjeev Mittu Pdf

This book is intended to give researchers and practitioners in the cross-cutting fields of artificial intelligence, machine learning (AI/ML) and cyber security up-to-date and in-depth knowledge of recent techniques for improving the vulnerabilities of AI/ML systems against attacks from malicious adversaries. The ten chapters in this book, written by eminent researchers in AI/ML and cyber-security, span diverse, yet inter-related topics including game playing AI and game theory as defenses against attacks on AI/ML systems, methods for effectively addressing vulnerabilities of AI/ML operating in large, distributed environments like Internet of Things (IoT) with diverse data modalities, and, techniques to enable AI/ML systems to intelligently interact with humans that could be malicious adversaries and/or benign teammates. Readers of this book will be equipped with definitive information on recent developments suitable for countering adversarial threats in AI/ML systems towards making them operate in a safe, reliable and seamless manner.

Machine Learning Techniques for Cybersecurity

Elisa Bertino,Sonam Bhardwaj,Fabrizio Cicala,Sishuai Gong,Imtiaz Karim,Charalampos Katsis,Hyunwoo Lee,Adrian Shuai Li,Ashraf Y. Mahgoub
Machine Learning Techniques for Cybersecurity Book PDF

Author : Elisa Bertino,Sonam Bhardwaj,Fabrizio Cicala,Sishuai Gong,Imtiaz Karim,Charalampos Katsis,Hyunwoo Lee,Adrian Shuai Li,Ashraf Y. Mahgoub
Publisher : Springer Nature
Page : 169 pages
File Size : 50,6 Mb
Release : 2023-04-08
Category : Computers
ISBN : 9783031282591

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Machine Learning Techniques for Cybersecurity by Elisa Bertino,Sonam Bhardwaj,Fabrizio Cicala,Sishuai Gong,Imtiaz Karim,Charalampos Katsis,Hyunwoo Lee,Adrian Shuai Li,Ashraf Y. Mahgoub Pdf

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.

Machine Learning Approaches in Cyber Security Analytics

Tony Thomas,Athira P. Vijayaraghavan,Sabu Emmanuel
Machine Learning Approaches in Cyber Security Analytics Book PDF

Author : Tony Thomas,Athira P. Vijayaraghavan,Sabu Emmanuel
Publisher : Springer Nature
Page : 217 pages
File Size : 43,7 Mb
Release : 2019-12-16
Category : Computers
ISBN : 9789811517068

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Machine Learning Approaches in Cyber Security Analytics by Tony Thomas,Athira P. Vijayaraghavan,Sabu Emmanuel Pdf

This book introduces various machine learning methods for cyber security analytics. With an overwhelming amount of data being generated and transferred over various networks, monitoring everything that is exchanged and identifying potential cyber threats and attacks poses a serious challenge for cyber experts. Further, as cyber attacks become more frequent and sophisticated, there is a requirement for machines to predict, detect, and identify them more rapidly. Machine learning offers various tools and techniques to automate and quickly predict, detect, and identify cyber attacks.

Machine Learning and Security

Clarence Chio,David Freeman
Machine Learning and Security Book PDF

Author : Clarence Chio,David Freeman
Publisher : "O'Reilly Media, Inc."
Page : 385 pages
File Size : 42,6 Mb
Release : 2018-01-26
Category : Computers
ISBN : 9781491979877

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Machine Learning and Security by Clarence Chio,David Freeman Pdf

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

Modern Approaches in IoT and Machine Learning for Cyber Security

Vinit Kumar Gunjan,Mohd Dilshad Ansari,Mohammed Usman,ThiDieuLinh Nguyen
Modern Approaches in IoT and Machine Learning for Cyber Security Book PDF

Author : Vinit Kumar Gunjan,Mohd Dilshad Ansari,Mohammed Usman,ThiDieuLinh Nguyen
Publisher : Springer Nature
Page : 415 pages
File Size : 51,8 Mb
Release : 2024-01-08
Category : Technology & Engineering
ISBN : 9783031099557

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Modern Approaches in IoT and Machine Learning for Cyber Security by Vinit Kumar Gunjan,Mohd Dilshad Ansari,Mohammed Usman,ThiDieuLinh Nguyen Pdf

This book examines the cyber risks associated with Internet of Things (IoT) and highlights the cyber security capabilities that IoT platforms must have in order to address those cyber risks effectively. The chapters fuse together deep cyber security expertise with artificial intelligence (AI), machine learning, and advanced analytics tools, which allows readers to evaluate, emulate, outpace, and eliminate threats in real time. The book’s chapters are written by experts of IoT and machine learning to help examine the computer-based crimes of the next decade. They highlight on automated processes for analyzing cyber frauds in the current systems and predict what is on the horizon. This book is applicable for researchers and professionals in cyber security, AI, and IoT.

AI, Machine Learning and Deep Learning

Fei Hu,Xiali Hei
AI Machine Learning and Deep Learning Book PDF

Author : Fei Hu,Xiali Hei
Publisher : CRC Press
Page : 420 pages
File Size : 54,9 Mb
Release : 2023-06-05
Category : Computers
ISBN : 9781000878899

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AI, Machine Learning and Deep Learning by Fei Hu,Xiali Hei Pdf

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