10 Machine Learning Blueprints You Should Know for Cybersecurity PDF Download
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Author: Rajvardhan Oak
Publisher: Packt Publishing Ltd
ISBN: 1804611972
Category : Computers
Languages : en
Pages : 330
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Book Description
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.
Author: Rajvardhan Oak
Publisher: Packt Publishing Ltd
ISBN: 1804611972
Category : Computers
Languages : en
Pages : 330
Get Book Here
Book Description
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.
Author: Hariom Tatsat
Publisher: "O'Reilly Media, Inc."
ISBN: 1492073008
Category : Computers
Languages : en
Pages : 426
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Book Description
Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You'll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
Author: Emmanuel Tsukerman
Publisher: Packt Publishing Ltd
ISBN: 1838556346
Category : Computers
Languages : en
Pages : 338
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Book Description
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.
Author: Cesar Bravo
Publisher: Packt Publishing Ltd
ISBN: 1800206097
Category : Computers
Languages : en
Pages : 528
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Book Description
An immersive learning experience enhanced with technical, hands-on labs to understand the concepts, methods, tools, platforms, and systems required to master the art of cybersecurity Key FeaturesGet hold of the best defensive security strategies and toolsDevelop a defensive security strategy at an enterprise levelGet hands-on with advanced cybersecurity threat detection, including XSS, SQL injections, brute forcing web applications, and moreBook Description Every organization has its own data and digital assets that need to be protected against an ever-growing threat landscape that compromises the availability, integrity, and confidentiality of crucial data. Therefore, it is important to train professionals in the latest defensive security skills and tools to secure them. Mastering Defensive Security provides you with in-depth knowledge of the latest cybersecurity threats along with the best tools and techniques needed to keep your infrastructure secure. The book begins by establishing a strong foundation of cybersecurity concepts and advances to explore the latest security technologies such as Wireshark, Damn Vulnerable Web App (DVWA), Burp Suite, OpenVAS, and Nmap, hardware threats such as a weaponized Raspberry Pi, and hardening techniques for Unix, Windows, web applications, and cloud infrastructures. As you make progress through the chapters, you'll get to grips with several advanced techniques such as malware analysis, security automation, computer forensics, and vulnerability assessment, which will help you to leverage pentesting for security. By the end of this book, you'll have become familiar with creating your own defensive security tools using IoT devices and developed advanced defensive security skills. What you will learnBecome well versed with concepts related to defensive securityDiscover strategies and tools to secure the most vulnerable factor – the userGet hands-on experience using and configuring the best security toolsUnderstand how to apply hardening techniques in Windows and Unix environmentsLeverage malware analysis and forensics to enhance your security strategySecure Internet of Things (IoT) implementationsEnhance the security of web applications and cloud deploymentsWho this book is for This book is for all IT professionals who want to take their first steps into the world of defensive security; from system admins and programmers to data analysts and data scientists with an interest in security. Experienced cybersecurity professionals working on broadening their knowledge and keeping up to date with the latest defensive developments will also find plenty of useful information in this book. You'll need a basic understanding of networking, IT, servers, virtualization, and cloud platforms before you get started with this book.
Author: Soma Halder
Publisher: Packt Publishing Ltd
ISBN: 178899096X
Category : Computers
Languages : en
Pages : 306
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Book Description
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
Author: Markus Christen
Publisher: Springer Nature
ISBN: 3030290530
Category : Philosophy
Languages : en
Pages : 388
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Book Description
This open access book provides the first comprehensive collection of papers that provide an integrative view on cybersecurity. It discusses theories, problems and solutions on the relevant ethical issues involved. This work is sorely needed in a world where cybersecurity has become indispensable to protect trust and confidence in the digital infrastructure whilst respecting fundamental values like equality, fairness, freedom, or privacy. The book has a strong practical focus as it includes case studies outlining ethical issues in cybersecurity and presenting guidelines and other measures to tackle those issues. It is thus not only relevant for academics but also for practitioners in cybersecurity such as providers of security software, governmental CERTs or Chief Security Officers in companies.
Author: Yuxi (Hayden) Liu
Publisher: Packt Publishing Ltd
ISBN: 1789617553
Category : Mathematics
Languages : en
Pages : 370
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Book Description
Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn Key FeaturesExploit the power of Python to explore the world of data mining and data analyticsDiscover machine learning algorithms to solve complex challenges faced by data scientists todayUse Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projectsBook Description The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML. Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way. With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more. By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities. What you will learnUnderstand the important concepts in machine learning and data scienceUse Python to explore the world of data mining and analyticsScale up model training using varied data complexities with Apache SparkDelve deep into text and NLP using Python libraries such NLTK and gensimSelect and build an ML model and evaluate and optimize its performanceImplement ML algorithms from scratch in Python, TensorFlow, and scikit-learnWho this book is for If you’re a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.
Author: Nicholas A Heard
Publisher: World Scientific
ISBN: 178634565X
Category : Computers
Languages : en
Pages : 305
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Book Description
Cyber-security is a matter of rapidly growing importance in industry and government. This book provides insight into a range of data science techniques for addressing these pressing concerns.The application of statistical and broader data science techniques provides an exciting growth area in the design of cyber defences. Networks of connected devices, such as enterprise computer networks or the wider so-called Internet of Things, are all vulnerable to misuse and attack, and data science methods offer the promise to detect such behaviours from the vast collections of cyber traffic data sources that can be obtained. In many cases, this is achieved through anomaly detection of unusual behaviour against understood statistical models of normality.This volume presents contributed papers from an international conference of the same name held at Imperial College. Experts from the field have provided their latest discoveries and review state of the art technologies.
Author: Khalid S. Soliman
Publisher: Springer Nature
ISBN: 3031628438
Category :
Languages : en
Pages : 415
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Book Description
Author: Ed Moyle
Publisher: Packt Publishing Ltd
ISBN: 1838982191
Category : Computers
Languages : en
Pages : 418
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Book Description
Plan and design robust security architectures to secure your organization's technology landscape and the applications you develop Key Features Leverage practical use cases to successfully architect complex security structures Learn risk assessment methodologies for the cloud, networks, and connected devices Understand cybersecurity architecture to implement effective solutions in medium-to-large enterprises Book DescriptionCybersecurity architects work with others to develop a comprehensive understanding of the business' requirements. They work with stakeholders to plan designs that are implementable, goal-based, and in keeping with the governance strategy of the organization. With this book, you'll explore the fundamentals of cybersecurity architecture: addressing and mitigating risks, designing secure solutions, and communicating with others about security designs. The book outlines strategies that will help you work with execution teams to make your vision a concrete reality, along with covering ways to keep designs relevant over time through ongoing monitoring, maintenance, and continuous improvement. As you progress, you'll also learn about recognized frameworks for building robust designs as well as strategies that you can adopt to create your own designs. By the end of this book, you will have the skills you need to be able to architect solutions with robust security components for your organization, whether they are infrastructure solutions, application solutions, or others.What you will learn Explore ways to create your own architectures and analyze those from others Understand strategies for creating architectures for environments and applications Discover approaches to documentation using repeatable approaches and tools Delve into communication techniques for designs, goals, and requirements Focus on implementation strategies for designs that help reduce risk Become well-versed with methods to apply architectural discipline to your organization Who this book is for If you are involved in the process of implementing, planning, operating, or maintaining cybersecurity in an organization, then this security book is for you. This includes security practitioners, technology governance practitioners, systems auditors, and software developers invested in keeping their organizations secure. If you’re new to cybersecurity architecture, the book takes you through the process step by step; for those who already work in the field and have some experience, the book presents strategies and techniques that will help them develop their skills further.
