Download or read book Cybernetics Intelligence With Python written by Prof. Frank Appiah and published by Lulu.com. This book was released on with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Python for Cybersecurity written by Howard E. Poston, III and published by John Wiley & Sons. This book was released on 2022-02-01 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover an up-to-date and authoritative exploration of Python cybersecurity strategies Python For Cybersecurity: Using Python for Cyber Offense and Defense delivers an intuitive and hands-on explanation of using Python for cybersecurity. It relies on the MITRE ATT&CK framework to structure its exploration of cyberattack techniques, attack defenses, and the key cybersecurity challenges facing network administrators and other stakeholders today. Offering downloadable sample code, the book is written to help you discover how to use Python in a wide variety of cybersecurity situations, including: Reconnaissance, resource development, initial access, and execution Persistence, privilege escalation, defense evasion, and credential access Discovery, lateral movement, collection, and command and control Exfiltration and impact Each chapter includes discussions of several techniques and sub-techniques that could be used to achieve an attacker's objectives in any of these use cases. The ideal resource for anyone with a professional or personal interest in cybersecurity, Python For Cybersecurity offers in-depth information about a wide variety of attacks and effective, Python-based defenses against them.

Download or read book Python Machine Learning written by Ryan Turner and published by Publishing Factory . This book was released on 2020-04-18 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you need a general purpose, high level programming language? Do you want something that which focuses on readability and has less lines of codes than other programming languages? This book is one that provides that! Python is one of the best machine learning concepts currently on the market and it has seen a spike in popularity, mainly due to its simplicity when it comes to working with machine learning algorithms. Inside the pages of Python Machine Learning: The Ultimate Intermediate Guide to Learn Python Machine Learning Step by Step Using Scikit-learn and Tensorflow you will find easy to understand information which is perfect for those who want to take the next steps in their programming journey and includes: - The principles surrounding Python - Different types of networks so you can choose what works best for you - Features of the system - Real world feature engineering - Understanding the techniques of semi-supervised learning - And much more… If you already have some basic knowledge of Python, the various programming models and functional programming it supports, then this intermediate guide is perfect for expanding your knowledge base. Get your copy of this amazing book today and increase your Python skills now!

Download or read book Cybernetics Cognition and Machine Learning Applications written by Vinit Kumar Gunjan and published by Springer Nature. This book was released on 2021-03-30 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes the original, peer reviewed research articles from the 2nd International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA 2020), held in August, 2020 at Goa, India. It covers the latest research trends or developments in areas of data science, artificial intelligence, neural networks, cognitive science and machine learning applications, cyber physical systems and cybernetics.

Download or read book Python Machine Learning written by Ryan Turner and published by Publishing Factory . This book was released on 2020-04-12 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you a novice programmer who wants to learn Python Machine Learning? Are you worried about how to translate what you already know into Python? This book will help you overcome those problems. As machines get ever more complex and perform more and more tasks to free up our time, so it is that new ideas are developed to help us continually improve their speed and abilities. One of these is Python and in Python Machine Learning: The Ultimate Beginner's Guide to Learn Python Machine Learning Step by Step using Scikit-Learn and Tensorflow, you will discover information and advice on: • What machine learning is • The history of machine learning • Approaches to machine learning • Support vector machines • Machine learning and neural networks • The Internet of Things (IoT) • The future of machine learning • And more… This book has been written specifically for beginners and the simple, step by step instructions and plain language make it an ideal place to start for anyone who has a passing interest in this fascinating subject. Python really is an amazing system and can provide you with endless possibilities when you start learning about it. Get a copy of Python Machine Learning today and see where the future lies!

Download or read book Cybercat Simulation in Python written by Dr. Frank Appiah and published by Lulu.com. This book was released on with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Python Machine Learning written by Nelson Holden and published by . This book was released on 2022-05-07 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Continue reading if you want to learn how to rapidly and simply create and master various Machine Learning algorithms. We are now living in the age of Artificial Intelligence. Self-driving vehicles, personalized product suggestions, real-time pricing, voice and face recognition are just a few instances that demonstrate this point. Consider medical diagnostics or the automation of boring and repetitive labor duties; these demonstrate that we live in exciting times. A lot is going on in Machine Learning, from academic subjects to projects and applications in various phases of development. Starting from the ground up, Python Machine Learning shows how this occurs, how computers gain experience, and how knowledge is compounded. Data is at the heart of Machine Learning because it contains realities beyond human comprehension. The calculations that computers can do on data are remarkable, far beyond what the human brain is capable of. Once we have introduced data to a machine learning model, we must construct an environment in which the data stream is updated regularly. This improves the machine's learning capability. The more data Machine Learning models are exposed to, the simpler it is for these models to grow in power. Inside, we'll talk about a variety of subjects, including: What is Machine Learning, and how is it used in real-world situations? Models for machine learning training How to Use Python Lists and Modules Python's 12 Must-Have Machine Learning Libraries What exactly is the Tensorflow library? Artificial Neural Networks (ANNs) And a lot more! Do you want to learn more? Scroll up and click the BUY NOW button to get your book right now!

Download or read book Hands On Machine Learning for Cybersecurity written by Soma Halder and published by Packt Publishing Ltd. This book was released on 2018-12-31 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Machine Learning with Python written by William Dimick and published by . This book was released on 2021-03-13 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you want to find out how to use Python code to manage and improve Artificial intelligence and Deep learning? Are you looking for an easy training guide for programmers and data scientists? If yes, then keep reading... Artificial intelligence is a branch of computer science that seeks to develop computer systems that are capable of human-like intelligence. You can have artificial intelligence that replicates the human mind implemented this way rather than just having a computer system that mimics and the entire human brain. The latter is probably something very far off, if it is ever achieved. Artificially intelligent systems run independently. Computer systems based on artificial intelligence need to be trained, but once trained, they can operate on their own without human intervention. In the case of human intelligence, the more data you are exposed to, the better you get at solving problems related to that data. Similarly, computer systems based on artificial intelligence self-adjust to make themselves perform better. This is quite a contrast with conventional computer systems, which only do what you tell them to do, and without humans rewriting the programs that run them, they don't get any better at what they do. This is a crucial point to focus on, because the kinds of systems that we are going to talk about in this book will adjust themselves and get better, without any human intervention whatsoever. Once they are deployed, the human operators might not even understand why the artificially intelligent computer system makes the decisions it does, or how it is making those decisions. This book covers: - Machine Learning - Concepts and Terms - Data Scrubbing - Data Mining Categories - Difference between Machine Learning and AI So, ready to get started? Order Now!

Download or read book Introduction to Machine Learning in the Cloud with Python written by Pramod Gupta and published by Springer Nature. This book was released on 2021-04-28 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to machine learning and cloud computing, both 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.

Download or read book Python For Cybersecurity written by Dr Hesham Mohamed Elsherif and published by . This book was released on 2024-04-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the evolving digital landscape, the importance of cybersecurity cannot be overstated. As threats become more sophisticated and pervasive, the demand for skilled professionals who can navigate and secure our digital spaces has surged. "Python for Cybersecurity" is crafted to bridge the gap between theoretical knowledge and practical skills, providing readers with the tools necessary to protect digital assets in an increasingly vulnerable online world. Python, with its simplicity and versatility, stands as a cornerstone for those venturing into the realm of cybersecurity. This book is designed to leverage Python's capabilities to build a strong foundation in cybersecurity principles, practices, and techniques. Whether you are a beginner with a keen interest in cybersecurity or an experienced professional looking to expand your toolkit, this book offers a comprehensive journey into the heart of cybersecurity practices using Python. The journey begins with an introduction to Python, focusing on aspects most relevant to cybersecurity. Readers new to Python will find this section a crash course that brings them up to speed, while experienced programmers will appreciate the refresher and the focus on cybersecurity applications. We cover basic programming concepts, data structures, and Python libraries that are pivotal for cybersecurity tasks. Subsequent chapters delve into the practical applications of Python in cybersecurity. We explore how Python can be used for developing tools and scripts that automate the detection of vulnerabilities, perform network analysis, and simulate cyber attacks to test the resilience of systems. Each chapter is filled with real-world examples and hands-on exercises designed to reinforce the concepts discussed. Advanced topics are not left behind, as we venture into areas such as cryptography, penetration testing, and forensic analysis using Python. These chapters aim to equip readers with the skills necessary to design, implement, and deploy Python-based solutions in response to complex cybersecurity challenges. "Python for Cybersecurity" also emphasizes the ethical considerations and legal frameworks surrounding cybersecurity. It is crucial for practitioners to operate within these boundaries, and this book provides the guidance needed to navigate these complex waters. Finally, the book concludes with a discussion on the future of cybersecurity and the role Python is poised to play in this dynamic field. We explore emerging threats and the latest Python tools and libraries developed to counteract these risks. This section prepares readers for what lies ahead, ensuring that readers are not just proficient with current technologies but are also ready to adapt and evolve with the cybersecurity landscape. Whether you aim to protect personal data, secure corporate networks, or contribute to national security efforts, "Python for Cybersecurity" is your comprehensive guide to mastering the skills necessary for success in this critical field. Welcome to the journey of becoming a proficient Python cybersecurity professional. Enjoy Learning! Dr. Hesham Mohamed Elsherif

Download or read book Python for OSINT written by Jason Bourny and published by Independently Published. This book was released on 2024-05-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python for OSINT: Tracking and Profiling Targets Unleash the Power of Python for Open Source Intelligence! Are you ready to elevate your cyber intelligence skills? "Python for OSINT: Tracking and Profiling Targets" is your essential guide to mastering the art of open-source intelligence (OSINT) using the Python programming language. Designed for hackers, pentesters, and cybersecurity professionals, this book equips you with the tools and techniques to uncover and analyze valuable information from publicly available sources. Key Features and Benefits: Advanced Web Scraping Dive deep into sophisticated web scraping methods. Learn how to extract valuable data from websites efficiently, bypassing common obstacles such as CAPTCHAs and anti-scraping mechanisms. This book provides you with the knowledge to collect and process vast amounts of data quickly using Python, Bash scripting, and PowerShell. Comprehensive Data Extraction Master the art of data extraction from various online sources, including social media platforms, forums, and databases. Understand how to use Python libraries and tools to gather intelligence and profile targets effectively. Techniques for network security, steganography, and cryptography are also covered. Real-World OSINT Projects Engage with practical, hands-on projects that simulate real-world scenarios. Each chapter includes exercises and examples that take you from theory to practice, ensuring you gain actionable skills. Projects include Python automation, hacking tools, and data extraction from IoT devices. Python Programming for Intelligence Gathering Whether you're a beginner or an experienced programmer, this book offers a thorough introduction to Python, focusing on its application in OSINT. Learn to write powerful scripts that automate the process of tracking and profiling targets. Explore advanced Python projects, Python machine learning, and how to run a Python script effectively. Ethical Hacking and Compliance Understand the ethical considerations and legal boundaries of OSINT. This book emphasizes responsible usage of intelligence-gathering techniques, ensuring you stay within legal and ethical limits while conducting investigations. Insights into black hat hacking, gray hat Python, and ethical hacking books are included. Cutting-Edge Techniques Stay ahead of the game with the latest OSINT methodologies and tools. This book is continuously updated to include new strategies and technologies, ensuring you're always equipped with the most current knowledge. Topics like black web, Bluetooth device hacking, and micropython are covered. Why Choose This Book? "Python for OSINT" is not just another technical manual; it's your pathway to becoming a proficient intelligence analyst. Written by industry experts, this book simplifies complex concepts into clear, actionable steps, making it accessible for both novices and seasoned professionals. Who Should Read This Book? Aspiring Hackers: Start with a solid foundation in OSINT techniques and tools. Pentesters: Enhance your skill set with advanced intelligence-gathering strategies. Cybersecurity Professionals: Stay updated with the latest OSINT techniques to protect your organization effectively. Python Enthusiasts: Leverage your programming skills to gather and analyze intelligence like a pro. Propel Your Cyber Intelligence Career Forward Invest in your future by mastering the art of OSINT with Python. "Python for OSINT: Tracking and Profiling Targets" is your indispensable resource for becoming a leader in the field of cyber intelligence. Don't miss out on this essential guide. Add it to your cart now and take the first step towards becoming an OSINT expert!

Download or read book Python for Data Analysis written by Mik Arduino and published by Charlie Creative Lab. This book was released on 2020-11-28 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you want to become the best programmer among your competitors and outperform the competition with a clear and complete approach without becoming frustrated trying to understand the big data, then keep reading... Computer programmers are increasingly in demand in the world. It is estimated that over 50% of companies fail to hire Python programmers because there are not enough advanced and prepared programmers. Don't you think it is smart to have adequate training regarding the Python language to outperform the competition and be able to work always and in every situation, even with the competition in today's world? The problem, however, is that the world is full of books and technical courses that are not able to easily explain the Python programming language and teach how to analyze the data properly. For this reason, it is not your fault, it is simply difficult for a programmer to become really good if he does not have clear and practical training available on such a technical and complicated subject. In fact, the aim of Python for Data Analysis is to make you understand data analysis, Python language, and the like in a simple way in which you won't be able to say, "It's too complicated to understand." What are some points you will learn in this book? The Basics of Python to Make You Independent and Start Earning a Salary by Letting You Take on Companies without Problems Intermediate Python Skills to Get You Above the Competition and Start an Important Career Advanced Python Skills to Become One of Your City's Best Programmers and Earn a 6-Digit Annual Salary Super Intensive Notions of Python to Make You One of the Most Experienced and Coveted Programmers by Companies and Get to be Able to Ask up to $ 200,000 a Year in Salary The 5 Fundamental Steps to Make You Able to Analyze Data Impeccably. The process is everything! The Importance of Big Data and How it Will Make You a Truly Expert Programmer Machine Learning...Deep Analysis Pandas Library Course to Rise above the Competition You Will Finally be Able to Understand the Matplotlib as if It Were the Simplest Thing in the World Python for Data Analysis is ideal for those who want a complete education explained in a simple language and in a detailed way about Python and data analysis, from the beginner course to the advanced course. This book is for you even if the programming languages are Arabic for you and you think you can't understand anything. Would You Like to Know More? Buy now to find out about Python for Data Analysis.

Download or read book Python Machine Learning Illustrated Guide For Beginners Intermediates written by William Sullivan and published by PublishDrive. This book was released on 2019-08-20 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python Machine Learning Illustrated Guide For Beginners & Intermediates Machines Can Learn ?! Automation and systematization is taking over the world. Slowly but surely we continuously see the rapid expansion of artificial intelligence, self-check out cash registers, automated phone lines, people-less car-washes , etc. The world is changing, find out how python programming ties into machine learning so you don't miss out on this next big trend! This is your beginner's step by step guide with illustrated pictures! Let's face it, machine learning is here to stay for the foreseeable future and will impact the lives billions worldwide! Drastically changing the world we live in the most fundamental ways, from our perceptions, life-style, thinking and in other aspects as well. What You Will Learn Linear & Polynomial Regression Support Vector Machines Decision Trees Random Forest KNN Algorithm Naive Bayes Algorithm Unsupervised Learning Clustering Cross Validation Grid Search And, much, much more! If you want to learn more about python machine learning it is highly recommended you start from the ground up by using this book. Normally books on this subject matter are expensive! Why not start off by making a small and affordable investment with your illustrated beginners guide that walks you through python machine learning step by step Why choose this book? Addresses Fundamental Concepts Goes Straight To The Point, uNo fluff or Nonsense Practical Examples High Quality Diagrams "Noob friendly" (Good For Beginners & Intermediates) Contains Various Aspects of Machine Learning Endorses Learn "By Doing Approach" Concise And To The Point I been working tirelessly to provide you quality books at an affordable price. I believe this book will give you the confidence to tackle python machine learning at a fundamental level. What are you waiting for? Make the greatest investment in YOUR knowledge base right now. Buy your copy now!

Download or read book Python AI Programming written by Patrick J and published by GitforGits. This book was released on 2024-01-03 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aspires young graduates and programmers to become AI engineers and enter the world of artificial intelligence by combining powerful Python programming with artificial intelligence. Beginning with the fundamentals of Python programming, the book gradually progresses to machine learning, where readers learn to implement Python in developing predictive models. The book provides a clear and accessible explanation of machine learning, incorporating practical examples and exercises that strengthen understanding. We go deep into deep learning, another vital component of AI. Readers gain a thorough understanding of how Python's frameworks and libraries can be used to create sophisticated neural networks and algorithms, which are required for tasks such as image and speech recognition. Natural Language Processing is also covered in the book, with fundamental concepts and techniques for interpreting and generating human-like language covered. The book's focus on computer vision and reinforcement learning is distinctive, presenting these cutting-edge AI fields in an approachable manner. Readers will learn how to use Python's intuitive programming paradigm to create systems that interpret visual data and make intelligent decisions based on environmental interactions. The book focuses on ethical AI development and responsible programming, emphasizing the importance of developing AI that is fair, transparent, and accountable. Each chapter is designed to improve learning by including practical examples, case studies, and exercises that provide hands-on experience. This book is an excellent starting point for anyone interested in becoming an AI engineer, providing the necessary foundational knowledge and skills to delve into the fascinating world of artificial intelligence. Key Learnings Explore Python basics and AI integration for real-world application and career advancement. Experience the power of Python in AI with practical machine learning techniques. Practice Python's deep learning tools for innovative AI solution development. Dive into NLP with Python to revolutionize data interpretation and communication strategies. Simple yet practical understanding of reinforcement learning for strategic AI decision making. Uncover ethical AI development and frameworks, and concepts of responsible and trustworthy AI. Harness Python's capabilities for creating AI applications with a focus on fairness and bias. Table of Content Introduction to Artificial Intelligence Python for AI Data as Fuel for AI Machine Learning Foundation Essentials of Deep Learning NLP and Computer Vision Hands-on Reinforcement Learning Ethics to AI

Download or read book Python Advanced Guide to Artificial Intelligence written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2018-12-21 with total page 748 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key FeaturesMaster supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and moreBuild, deploy, and scale end-to-end deep neural network models in a production environmentBook Description This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: Mastering Machine Learning Algorithms by Giuseppe BonaccorsoMastering TensorFlow 1.x by Armando FandangoDeep Learning for Computer Vision by Rajalingappaa ShanmugamaniWhat you will learnExplore how an ML model can be trained, optimized, and evaluatedWork with Autoencoders and Generative Adversarial NetworksExplore the most important Reinforcement Learning techniquesBuild end-to-end deep learning (CNN, RNN, and Autoencoders) modelsWho this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.

Download or read book Reinforcement Learning for Cyber Physical Systems written by Chong Li and published by CRC Press. This book was released on 2019-02-22 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was inspired by recent developments in the fields of reinforcement learning (RL) and cyber-physical systems (CPSs). Rooted in behavioral psychology, RL is one of the primary strands of machine learning. Different from other machine learning algorithms, such as supervised learning and unsupervised learning, the key feature of RL is its unique learning paradigm, i.e., trial-and-error. Combined with the deep neural networks, deep RL become so powerful that many complicated systems can be automatically managed by AI agents at a superhuman level. On the other hand, CPSs are envisioned to revolutionize our society in the near future. Such examples include the emerging smart buildings, intelligent transportation, and electric grids. However, the conventional hand-programming controller in CPSs could neither handle the increasing complexity of the system, nor automatically adapt itself to new situations that it has never encountered before. The problem of how to apply the existing deep RL algorithms, or develop new RL algorithms to enable the real-time adaptive CPSs, remains open. This book aims to establish a linkage between the two domains by systematically introducing RL foundations and algorithms, each supported by one or a few state-of-the-art CPS examples to help readers understand the intuition and usefulness of RL techniques. Features Introduces reinforcement learning, including advanced topics in RL Applies reinforcement learning to cyber-physical systems and cybersecurity Contains state-of-the-art examples and exercises in each chapter Provides two cybersecurity case studies Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies is an ideal text for graduate students or junior/senior undergraduates in the fields of science, engineering, computer science, or applied mathematics. It would also prove useful to researchers and engineers interested in cybersecurity, RL, and CPS. The only background knowledge required to appreciate the book is a basic knowledge of calculus and probability theory.