Verfügbarkeit: Hands-on artificial intelligence for cybersecurity :

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...

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Bibliographische Detailangaben
1. Verfasser:	Parisi, Alessandro (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Birmingham, UK : Packt Publishing, 2019.
Schlagworte:	Computer security. Machine learning. Sécurité informatique. Apprentissage automatique. Computer security Machine learning Electronic book.
Online-Zugang:	Volltext
Zusammenfassung:	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.
Beschreibung:	A Bayesian spam detector with NLTK
Beschreibung:	1 online resource (331 pages)
Bibliographie:	Includes bibliographical references and index.
ISBN:	1789805171 9781789805178

Internformat

MARC


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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).
650		0	\|a Computer security. \|0 http://id.loc.gov/authorities/subjects/sh90001862
650		0	\|a Machine learning. \|0 http://id.loc.gov/authorities/subjects/sh85079324
650		6	\|a Sécurité informatique.
650		6	\|a Apprentissage automatique.
650		7	\|a Computer security \|2 fast
650		7	\|a Machine learning \|2 fast
655		4	\|a Electronic book.
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	\|l FWS01 \|p ZDB-4-EBA \|q FWS_PDA_EBA \|u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2225815 \|3 Volltext
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Datensatz im Suchindex

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contents	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 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 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 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 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
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spelling	Parisi, Alessandro, author. Hands-on artificial intelligence for cybersecurity : implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies / Alessandro Parisi. Birmingham, UK : Packt Publishing, 2019. ©2019 1 online resource (331 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. 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 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 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 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 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 A Bayesian spam detector with NLTK 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. Online resource; title from digital title page (viewed on December 27, 2019). Computer security. http://id.loc.gov/authorities/subjects/sh90001862 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Sécurité informatique. Apprentissage automatique. Computer security fast Machine learning fast Electronic book. has work: Hands-On Artificial Intelligence for Cybersecurity (Text) https://id.oclc.org/worldcat/entity/E39PD3hjHPJKrprGKRBJg7jBRq https://id.oclc.org/worldcat/ontology/hasWork Print version: Parisi, Alessandro. Hands-On Artificial Intelligence for Cybersecurity : Implement Smart AI Systems for Preventing Cyber Attacks and Detecting Threats and Network Anomalies. Birmingham : Packt Publishing, Limited, ©2019 9781789804027 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2225815 Volltext
spellingShingle	Parisi, Alessandro Hands-on artificial intelligence for cybersecurity : implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies / 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 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 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 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 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 Computer security. http://id.loc.gov/authorities/subjects/sh90001862 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Sécurité informatique. Apprentissage automatique. Computer security fast Machine learning fast
subject_GND	http://id.loc.gov/authorities/subjects/sh90001862 http://id.loc.gov/authorities/subjects/sh85079324
title	Hands-on artificial intelligence for cybersecurity : implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies /
title_auth	Hands-on artificial intelligence for cybersecurity : implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies /
title_exact_search	Hands-on artificial intelligence for cybersecurity : implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies /
title_full	Hands-on artificial intelligence for cybersecurity : implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies / Alessandro Parisi.
title_fullStr	Hands-on artificial intelligence for cybersecurity : implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies / Alessandro Parisi.
title_full_unstemmed	Hands-on artificial intelligence for cybersecurity : implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies / Alessandro Parisi.
title_short	Hands-on artificial intelligence for cybersecurity :
title_sort	hands on artificial intelligence for cybersecurity implement smart ai systems for preventing cyber attacks and detecting threats and network anomalies
title_sub	implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies /
topic	Computer security. http://id.loc.gov/authorities/subjects/sh90001862 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Sécurité informatique. Apprentissage automatique. Computer security fast Machine learning fast
topic_facet	Computer security. Machine learning. Sécurité informatique. Apprentissage automatique. Computer security Machine learning Electronic book.
url	https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2225815
work_keys_str_mv	AT parisialessandro handsonartificialintelligenceforcybersecurityimplementsmartaisystemsforpreventingcyberattacksanddetectingthreatsandnetworkanomalies

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