Do you feel that informatics is indispensable in today's increasingly digital world? Do you want to introduce yourself to the world of programming or cyber security but don't know where to get started? If the answer to these questions is yes, then keep reading...
This book includes:
PYTHON MACHINE LEARNING: A Beginner's Guide to Python Programming for Machine Learning and Deep Learning, Data Analysis, Algorithms and Data Science with Scikit Learn, TensorFlow, PyTorch and Keras
Here's a sneak peek of what you'll learn with this book:
- The Fundamentals of Python
- Python for Machine Learning
- Data Analysis in Python
- Comparing Deep Learning and Machine Learning
- The Role of Machine Learning in the Internet of Things (IoT)
And much more...
SQL FOR BEGINNERS: A Step by Step Guide to Learn SQL Programming for Query Performance Tuning on SQL Database
Throughout these pages, you will learn:
- How to build databases and tables with the data you create.
- How to sort through the data efficiently to find what you need.
- The exact steps to clean your data and make it easier to analyze.
- How to modify and delete tables and databases.
And much more...
LINUX FOR BEGINNERS: An Introduction to the Linux Operating System for Installation, Configuration and Command Line
We will cover the following topics:
- How to Install Linux
- The Linux Console
- Command line interface
- Network administration
And much more...
HACKING WITH KALI LINUX: A Beginner's Guide to Learn Penetration Testing to Protect Your Family and Business from Cyber Attacks Building a Home Security System for Wireless Network Security
You will learn:
- The importance of cybersecurity
- How malware and cyber-attacks operate
- How to install Kali Linux on a virtual box
- VPNs & Firewalls
And much more...
ETHICAL HACKING: A Beginner's Guide to Computer and Wireless Networks Defense Strategies, Penetration Testing and Information Security Risk Assessment
Here's a sneak peek of what you'll learn with this book:
- What is Ethical Hacking (roles and responsibilities of an Ethical Hacker)
- Most common security tools
- The three ways to scan your system
- The seven proven penetration testing strategies
...and much more.
This book won't make you an expert programmer, but it will give you an exciting first look at programming and a foundation of basic concepts with which you can start your journey learning computer programming, machine learning and cybersecurity
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Автор: Abaimov Stanislav, Martellini Maurizio Название: Machine Learning for Cyber Agents: Attack and Defence ISBN: 3030915840 ISBN-13(EAN): 9783030915841 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Описание: This book introduces reinforcement learning, and provides novel ideas and use cases to demonstrate the benefits of using reinforcement learning for Cyber Physical Systems. Two important case studies on applying reinforcement learning to cybersecurity problems are included.
Автор: Brij B. Gupta, Quan Z. Sheng Название: Machine Learning for Computer and Cyber Security ISBN: 1138587303 ISBN-13(EAN): 9781138587304 Издательство: Taylor&Francis Рейтинг: Цена: 26796.00 р. Наличие на складе: Поставка под заказ.
Описание: This comprehensive book offers valuable insights while using a wealth of examples and illustrations to effectively demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security.
Автор: Iqbal Farkhund, Debbabi Mourad, Fung Benjamin C. M. Название: Machine Learning for Authorship Attribution and Cyber Forensics ISBN: 3030616746 ISBN-13(EAN): 9783030616748 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 1 CYBERSECURITY AND CYBERCRIME INVESTIGATION 1.1 CYBERSECURITY 1.2 KEY COMPONENTS TO MINIMIZING CYBERCRIMES 1.3 DAMAGE RESULTING FROM CYBERCRIME 1.4 CYBERCRIMES 1.4.1 Major Categories of Cybercrime 1.4.2 Causes of and Motivations for Cybercrime 1.5 MAJOR CHALLENGES 1.5.1 Hacker Tools and Exploit Kits 1.5.2 Universal Access 291.5.3 Online Anonymity 1.5.4 Organized Crime 301.5.5 Nation State Threat Actors 311.6 CYBERCRIME INVESTIGATION 322 MACHINE LEARNING FRAMEWORK FOR MESSAGING FORENSICS 342.1 SOURCES OF CYBERCRIMES 362.2 FEW ANALYSIS TOOLS AND TECHNIQUES 382.3 PROPOSED FRAMEWORK FOR CYBERCRIMES INVESTIGATION 392.4 AUTHORSHIP ANALYSIS 412.5 INTRODUCTION TO CRIMINAL INFORMATION MINING 432.5.1 Existing Criminal Information Mining Approaches 442.5.2 WordNet-based Criminal Information Mining 472.6 WEKA 483 HEADER-LEVEL INVESTIGATION AND ANALYZING NETWORK INFORMATION 503.1 STATISTICAL EVALUATION 523.2 TEMPORAL ANALYSIS 533.3 GEOGRAPHICAL LOCALIZATION 533.4 SOCIAL NETWORK ANALYSIS 553.5 CLASSIFICATION 563.6 CLUSTERING 584 AUTHORSHIP ANALYSIS APPROACHES 594.1 HISTORICAL PERSPECTIVE 594.2 ONLINE ANONYMITY AND AUTHORSHIP ANALYSIS 604.3 STYLOMETRIC FEATURES 614.4 AUTHORSHIP ANALYSIS METHODS 634.4.1 Statistical Analysis Methods 644.4.2 Machine Learning Methods 644.4.1 Classification Method Fundamentals 664.5 AUTHORSHIP ATTRIBUTION 674.6 AUTHORSHIP CHARACTERIZATION 694.7 AUTHORSHIP VERIFICATION 704.8 LIMITATIONS OF EXISTING AUTHORSHIP TECHNIQUES 725 AUTHORSHIP ANALYSIS - WRITEPRINT MINING FOR AUTHORSHIP ATTRIBUTION 745.1 AUTHORSHIP ATTRIBUTION PROBLEM 785.1.1 Attribution without Stylistic Variation 795.1.2 Attribution with Stylistic Variation 795.2 BUILDING BLOCKS OF THE PROPOSED APPROACH 805.3 WRITEPRINT 875.4 PROPOSED APPROACHES 875.4.1 AuthorMiner1: Attribution without Stylistic Variation 885.4.2 AuthorMiner2: Attribution with Stylistic Variation 926 AUTHORSHIP ATTRIBUTION WITH FEW TRAINING SAMPLES 976.1 PROBLEM STATEMENT AND FUNDAMENTALS 1006.2 PROPOSED APPROACH 1016.2.1 Preprocessing 1016.2.2 Clustering by Stylometric Features 1026.2.3 Frequent Stylometric Pattern Mining 1046.2.4 Writeprint Mining 1056.2.5 Identifying Author 1066.3 EXPERIMENTS AND DISCUSSION 1067 AUTHORSHIP CHARACTERIZATION 1137.1 PROPOSED APPROACH 1157.1.1 Clustering Anonymous Messages 1167.1.2 Extracting Writeprints from Sample Messages 1167.1.3 Identifying Author Characteristics 1167.2 EXPERIMENTS AND DISCUSSION 1178 AUTHORSHIP VERIFICATION 1208.1 PROBLEM STATEMENT 1238.2 PROPOSED APPROACH 1258.2.1 Verification by Classification 1268.2.2 Verification by Regression 1268.3 EXPERIMENTS AND DISCUSSION 1278.3.1 Verification by Classification. 1288.3.2 Verification by Regression 1289 AUTHORSHIP ATTRIBUTION USING CUSTOMIZED ASSOCIATIVE CLASSIFICATION 1319.1 PROBLEM STATEMENT 1329.1.1 Extracting Stylometric Features 1329.1.2 Associative Classification Writeprint 1339.1.3 Refined Problem Statement 1369.2 CLASSIFICATION BY MULTIPLE ASSOCIATION RULE FOR AUTHORSHIP ANALYSIS 1379.2.1 Mining Class Association Rules 1379.2.2 Pruning Class Association Rules 1399.2.3 Auth
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Автор: Misra, Siddharth Название: Machine Learning for Subsurface Characterization ISBN: 0128177365 ISBN-13(EAN): 9780128177365 Издательство: Elsevier Science Рейтинг: Цена: 18528.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
To continue to meet demand while keeping costs down, petroleum and reservoir engineers know it is critical to utilize their asset's data through more complex modeling methods, and machine learning and data analytics is the known alternative approach to accurately represent the complexity of fluid-filled rocks. With a lack of training resources available, Machine Learning for Subsurface Characterization focuses on the development and application of neural networks, deep learning, unsupervised learning, reinforcement learning, and clustering methods for subsurface characterization under constraints. Such constraints are encountered during subsurface engineering operations due to financial, operational, regulatory, risk, technological, and environmental challenges.
This reference teaches how to do more with less. Used to develop tools and techniques of data-driven predictive modelling and machine learning for subsurface engineering and science, engineers will be introduced to methods of generating subsurface signals and analyzing the complex relationships within various subsurface signals using machine learning. Algorithmic procedures in MATLAB, R, PYTHON, and TENSORFLOW are displayed in text and through online instructional video to assist training and learning. Field cases are also presented to understand real-world applications, with a particular focus on examples involving shale reservoirs.
Explaining the concept of machine learning, advantages to the industry, and applications applied to complex subsurface rocks, Machine Learning for Subsurface Characterization delivers a missing piece to the reservoir engineer's toolbox needed to support today's complex operations.
Focus on applying predictive modelling and machine learning from real case studies and Q&A sessions at the end of each chapter
Learn how to develop codes such as MATLAB, PYTHON, R, and TENSORFLOW with step-by-step guides included
Visually learn code development with video demonstrations included
Автор: Dinur Название: Cyber Security Cryptography and Machine Learning ISBN: 3319941461 ISBN-13(EAN): 9783319941462 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the Second International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2018, held in Beer-Sheva, Israel, in June 2018. The 16 full and 6 short papers presented in this volume were carefully reviewed and selected from 44 submissions.
Автор: Krishna P. Venkata, Gurumoorthy Sasikumar, Obaidat Mohammad S. Название: Social Network Forensics, Cyber Security, and Machine Learning ISBN: 9811314551 ISBN-13(EAN): 9789811314551 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book discusses the issues and challenges in Online Social Networks (OSNs). It highlights various aspects of OSNs consisting of novel social network strategies and the development of services using different computing models. Moreover, the book investigates how OSNs are impacted by cutting-edge innovations.
Описание: This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi`an, China in October 2020.The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions.
Название: Machine learning for computer and cyber security ISBN: 0367780275 ISBN-13(EAN): 9780367780272 Издательство: Taylor&Francis Рейтинг: Цена: 7961.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This comprehensive book offers valuable insights while using a wealth of examples and illustrations to effectively demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security.
Описание: This book addresses theories and empirical procedures for the application of machine learning and data mining to solve problems in cyber dynamics. and,disruptive technologies like blockchain. The book presents new machine learning and data mining approaches in solving problems in cyber dynamics.
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