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Hands-on artificial intelligence for cybersecurity : implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies /
If you wish to design smart, threat-proof cybersecurity systems using trending AI tools and techniques, then this book is for you. With this book, you will learn to develop intelligent systems that can detect suspicious patterns and attacks, thereby allowing you to protect your network and corporate...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 100 | 1 | |a Parisi, Alessandro, |e author. | |
| 245 | 1 | 0 | |a Hands-on artificial intelligence for cybersecurity : |b implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies / |c Alessandro Parisi. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2019. | |
| 264 | 4 | |c ©2019 | |
| 300 | |a 1 online resource (331 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Section 1: AI Core Concepts and Tools of the Trade; Chapter 1: Introduction to AI for Cybersecurity Professionals; Applying AI in cybersecurity; Evolution in AI: from expert systems to data mining; A brief introduction to expert systems; Reflecting the indeterministic nature of reality; Going beyond statistics toward machine learning; Mining data for models; Types of machine learning; Supervised learning; Unsupervised learning; Reinforcement learning; Algorithm training and optimization | |
| 505 | 8 | |a How to find useful sources of dataQuantity versus quality; Getting to know Python's libraries; Supervised learning example -- linear regression; Unsupervised learning example -- clustering; Simple NN example -- perceptron; AI in the context of cybersecurity; Summary; Chapter 2: Setting Up Your AI for Cybersecurity Arsenal; Getting to know Python for AI and cybersecurity; Python libraries for AI; NumPy as an AI building block; NumPy multidimensional arrays; Matrix operations with NumPy; Implementing a simple predictor with NumPy; Scikit-learn; Matplotlib and Seaborn; Pandas | |
| 505 | 8 | |a Python libraries for cybersecurityPefile; Volatility; Installing Python libraries; Enter Anaconda -- the data scientist's environment of choice; Anaconda Python advantages; Conda utility; Installing packages in Anaconda; Creating custom environments; Some useful Conda commands; Python on steroids with parallel GPU; Playing with Jupyter Notebooks; Our first Jupyter Notebook; Exploring the Jupyter interface; What's in a cell?; Useful keyboard shortcuts; Choose your notebook kernel; Getting your hands dirty; Installing DL libraries; Deep learning pros and cons for cybersecurity; TensorFlow; Keras | |
| 505 | 8 | |a PyTorchPyTorch versus TensorFlow; Summary; Section 2: Detecting Cybersecurity Threats with AI; Chapter 3: Ham or Spam? Detecting Email Cybersecurity Threats with AI; Detecting spam with Perceptrons; Meet NNs at their purest -- the Perceptron; It's all about finding the right weight!; Spam filters in a nutshell; Spam filters in action; Detecting spam with linear classifiers; How the Perceptron learns; A simple Perceptron-based spam filter; Pros and cons of Perceptrons; Spam detection with SVMs; SVM optimization strategy; SVM spam filter example; Image spam detection with SVMs | |
| 505 | 8 | |a How did SVM come into existence?Phishing detection with logistic regression and decision trees; Regression models; Introducing linear regression models; Linear regression with scikit-learn; Linear regression -- pros and cons; Logistic regression; A phishing detector with logistic regression; Logistic regression pros and cons; Making decisions with trees; Decision trees rationales; Phishing detection with decision trees; Decision trees -- pros and cons; Spam detection with Naive Bayes; Advantages of Naive Bayes for spam detection; Why Naive Bayes?; NLP to the rescue; NLP steps | |
| 500 | |a A Bayesian spam detector with NLTK | ||
| 520 | |a If you wish to design smart, threat-proof cybersecurity systems using trending AI tools and techniques, then this book is for you. With this book, you will learn to develop intelligent systems that can detect suspicious patterns and attacks, thereby allowing you to protect your network and corporate assets. | ||
| 588 | 0 | |a Online resource; title from digital title page (viewed on December 27, 2019). | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Computer security. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Sécurité informatique. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a Computer security |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 758 | |i has work: |a Hands-On Artificial Intelligence for Cybersecurity (Text) |1 https://id.oclc.org/worldcat/entity/E39PD3hjHPJKrprGKRBJg7jBRq |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Parisi, Alessandro. |t Hands-On Artificial Intelligence for Cybersecurity : Implement Smart AI Systems for Preventing Cyber Attacks and Detecting Threats and Network Anomalies. |d Birmingham : Packt Publishing, Limited, ©2019 |z 9781789804027 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5847212 |z Texto completo |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781789804027/?ar |z Texto completo |
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