Hands On Machine Learning For Cybersecurity
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Hands On Machine Learning For Cybersecurity
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Author : Soma Halder
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-12-31
Hands On Machine Learning For Cybersecurity written by Soma Halder 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 2018-12-31 with Computers categories.
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
Hands On Artificial Intelligence For Cybersecurity
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Author : Alessandro Parisi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-08-02
Hands On Artificial Intelligence For Cybersecurity written by Alessandro Parisi 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 2019-08-02 with Computers categories.
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.
Hands On Machine Learning With Ml Net
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Author : Jarred Capellman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-03-27
Hands On Machine Learning With Ml Net written by Jarred Capellman 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 2020-03-27 with Computers categories.
Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core Key FeaturesGet well-versed with the ML.NET framework and its components and APIs using practical examplesLearn how to build, train, and evaluate popular machine learning algorithms with ML.NET offeringsExtend your existing machine learning models by integrating with TensorFlow and other librariesBook Description Machine learning (ML) is widely used in many industries such as science, healthcare, and research and its popularity is only growing. In March 2018, Microsoft introduced ML.NET to help .NET enthusiasts in working with ML. With this book, you’ll explore how to build ML.NET applications with the various ML models available using C# code. The book starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You’ll then explore the ML.NET framework, its components, and APIs. The book will serve as a practical guide to helping you build smart apps using the ML.NET library. You’ll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each chapter will cover the practical implementation, showing you how to implement ML within .NET applications. You’ll also learn to integrate TensorFlow in ML.NET applications. Later you’ll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR. By the end of this book, you’ll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET. What you will learnUnderstand the framework, components, and APIs of ML.NET using C#Develop regression models using ML.NET for employee attrition and file classificationEvaluate classification models for sentiment prediction of restaurant reviewsWork with clustering models for file type classificationsUse anomaly detection to find anomalies in both network traffic and login historyWork with ASP.NET Core Blazor to create an ML.NET enabled web applicationIntegrate pre-trained TensorFlow and ONNX models in a WPF ML.NET application for image classification and object detectionWho this book is for If you are a .NET developer who wants to implement machine learning models using ML.NET, then this book is for you. This book will also be beneficial for data scientists and machine learning developers who are looking for effective tools to implement various machine learning algorithms. A basic understanding of C# or .NET is mandatory to grasp the concepts covered in this book effectively.
Machine Learning For Cyber Agents
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Author : Stanislav Abaimov
language : en
Publisher: Springer Nature
Release Date : 2022-01-27
Machine Learning For Cyber Agents written by Stanislav Abaimov and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-27 with Computers categories.
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 Red Team Hackers
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Author : Dr Emmanuel Tsukerman
language : en
Publisher: Independently Published
Release Date : 2020-08-15
Machine Learning For Red Team Hackers written by Dr Emmanuel Tsukerman and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-15 with categories.
Everyone knows that AI and machine learning are the future of penetration testing. Large cybersecurity enterprises talk about hackers automating and smartening their tools; The newspapers report on cybercriminals utilizing voice transfer technology to impersonate CEOs; The media warns us about the implications of DeepFakes in politics and beyond...This book finally teaches you how to use Machine Learning for Penetration Testing.This book will be teaching you, in a hands-on and practical manner, how to use the Machine Learning to perform penetration testing attacks, and how to perform penetration testing attacks ON Machine Learning systems. It will teach you techniques that few hackers or security experts know about.You will learn- how to supercharge your vulnerability fuzzing using Machine Learning.- how to evade Machine Learning malware classifiers.- how to perform adversarial attacks on commercially-available Machine Learning as a Service models.- how to bypass CAPTCHAs using Machine Learning.- how to create Deepfakes.- how to poison, backdoor and steal Machine Learning models.And you will solidify your slick new skills in fun hands-on assignments.
Artificial Intelligence For Cybersecurity
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Author : Mark Stamp
language : en
Publisher: Springer Nature
Release Date : 2022-07-15
Artificial Intelligence For Cybersecurity written by Mark Stamp and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-15 with Computers categories.
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.
Cyber Security Meets Machine Learning
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Author : Xiaofeng Chen
language : en
Publisher: Springer Nature
Release Date : 2021-07-02
Cyber Security Meets Machine Learning written by Xiaofeng Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-02 with Computers categories.
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 Security Beginners
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Author : Calvin Dolton
language : en
Publisher: Independently Published
Release Date : 2025-08-22
Machine Learning For Security Beginners written by Calvin Dolton and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-22 with Computers categories.
Unlock the power of machine learning for cybersecurity with this practical beginner's guide designed to help you build a strong foundation in both AI and security. Machine Learning for Security Beginners: Understanding AI and Machine Learning Basics to Build a Secure Foundation takes you step by step through the essentials-from math fundamentals and data preparation to building real-world phishing classifiers and anomaly detection systems with Scikit-learn and PyTorch. Instead of overwhelming theory, this book delivers hands-on projects, clear explanations, and working code that readers can execute and adapt to real security challenges. You'll explore phishing URL detection, intrusion detection with Isolation Forests, anomaly detection in network logs, and neural networks for malicious traffic-all while learning the core ML pipeline used by professionals. Written in a beginner-friendly but technically accurate style, this guide helps readers with a background in security or programming quickly transition into the growing field of AI-driven cybersecurity. Whether you want to strengthen your skills for a career move, understand modern security tools, or simply gain confidence in applying machine learning to real threats, this book gives you the clarity and confidence to succeed. What makes this book unique is its practical, security-first perspective. You won't just learn ML theory-you'll see how it applies to detecting phishing, spotting intrusions, and defending systems against evolving threats. Each chapter is structured to deliver immediate value, ensuring that readers not only understand concepts but also see them work in action. Calvin Dolton writes with authority at the intersection of machine learning, cybersecurity, and practical programming. With a clear, hands-on teaching approach, Dolton makes complex technology accessible for learners, bridging the gap between academic AI concepts and real-world security applications. His work emphasizes practical skill-building and credibility, making this book an essential starting point for anyone serious about learning machine learning for security.
Demystifying Ai And Ml For Cyber Threat Intelligence
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Author : Ming Yang
language : en
Publisher: Springer Nature
Release Date : 2025-08-16
Demystifying Ai And Ml For Cyber Threat Intelligence written by Ming Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-16 with Computers categories.
This book simplifies complex AI and ML concepts, making them accessible to security analysts, IT professionals, researchers, and decision-makers. Cyber threats have become increasingly sophisticated in the ever-evolving digital landscape, making traditional security measures insufficient to combat modern attacks. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in cybersecurity, enabling organizations to detect, prevent, and respond to threats with greater efficiency. This book is a comprehensive guide, bridging the gap between cybersecurity and AI/ML by offering clear, practical insights into their role in threat intelligence. Readers will gain a solid foundation in key AI and ML principles, including supervised and unsupervised learning, deep learning, and natural language processing (NLP) while exploring real-world applications such as intrusion detection, malware analysis, and fraud prevention. Through hands-on insights, case studies, and implementation strategies, it provides actionable knowledge for integrating AI-driven threat intelligence into security operations. Additionally, it examines emerging trends, ethical considerations, and the evolving role of AI in cybersecurity. Unlike overly technical manuals, this book balances theoretical concepts with practical applications, breaking down complex algorithms into actionable insights. Whether a seasoned professional or a beginner, readers will find this book an essential roadmap to navigating the future of cybersecurity in an AI-driven world. This book empowers its audience to stay ahead of cyber adversaries and embrace the next generation of intelligent threat detection.
Adversary Aware Learning Techniques And Trends In Cybersecurity
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Author : Prithviraj Dasgupta
language : en
Publisher: Springer Nature
Release Date : 2021-01-22
Adversary Aware Learning Techniques And Trends In Cybersecurity written by Prithviraj Dasgupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-22 with Computers categories.
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.