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Hands-On Artificial Intelligence for Cybersecurity: Implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies

Parisi

Free Trial

4.4 (5 Ratings)

Paperback Aug 2019 342 pages 1st Edition

Parisi

Free Trial

4.4 (5 Ratings)

Paperback Aug 2019 342 pages 1st Edition

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Key benefits

Identify and predict security threats using artificial intelligence
Develop intelligent systems that can detect unusual and suspicious patterns and attacks
Learn how to test the effectiveness of your AI cybersecurity algorithms and tools

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.

Who is this book 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.

What you will learn

Detect email threats such as spamming and phishing using AI
Categorize APT, zero-days, and polymorphic malware samples
Overcome antivirus limits in threat detection
Predict network intrusions and detect anomalies with machine learning
Verify the strength of biometric authentication procedures with deep learning
Evaluate cybersecurity strategies and learn how you can improve them

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Product Details

Publication date : Aug 02, 2019

Length: 342 pages

Edition : 1st

Language : English

ISBN-13 : 9781789804027

Category :

Data

Languages :

Python

Concepts :

Artificial Intelligence

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15 Chapters

Section 1: AI Core Concepts and Tools of the Trade

Introduction to AI for Cybersecurity Professionals

Applying AI in cybersecurity

Evolution in AI: from expert systems to data mining

Types of machine learning

Algorithm training and optimization

Getting to know Python's libraries

AI in the context of cybersecurity

Summary

Setting Up Your AI for Cybersecurity Arsenal

Getting to know Python for AI and cybersecurity

Python libraries for cybersecurity

Enter Anaconda – the data scientist's environment of choice

Playing with Jupyter Notebooks

Installing DL libraries

Summary

Section 2: Detecting Cybersecurity Threats with AI

Ham or Spam? Detecting Email Cybersecurity Threats with AI

Detecting spam with Perceptrons

Spam detection with SVMs

Phishing detection with logistic regression and decision trees

Spam detection with Naive Bayes

NLP to the rescue

Summary

Malware Threat Detection

Malware analysis at a glance

Telling different malware families apart

Decision tree malware detectors

Detecting metamorphic malware with HMMs

Advanced malware detection with deep learning

Summary

Network Anomaly Detection with AI

Network anomaly detection techniques

How to classify network attacks

Detecting botnet topology

Different ML algorithms for botnet detection

Summary

Section 3: Protecting Sensitive Information and Assets

Securing User Authentication

Authentication abuse prevention

Account reputation scoring

User authentication with keystroke recognition

Biometric authentication with facial recognition

Summary

Fraud Prevention with Cloud AI Solutions

Introducing fraud detection algorithms

Predictive analytics for credit card fraud detection

Getting to know IBM Watson Cloud solutions

Importing sample data and running Jupyter Notebook in the cloud

Evaluating the quality of our predictions

Summary

GANs - Attacks and Defenses

GANs in a nutshell

GAN Python tools and libraries

Network attack via model substitution

IDS evasion via GAN

Facial recognition attacks with GAN

Summary

Section 4: Evaluating and Testing Your AI Arsenal

Evaluating Algorithms

Best practices of feature engineering

Evaluating a detector's performance with ROC

How to split data into training and test sets

Using cross validation for algorithms

Summary

Assessing your AI Arsenal

Evading ML detectors

Challenging ML anomaly detection

Testing for data and model quality

Ensuring security and reliability

Summary

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Customer reviews

Rating distribution

4.4

(5 Ratings)

5 star 80%

4 star 0%

3 star 0%

2 star 20%

1 star 0%

Jan McSweeney Dec 01, 2019

Python has a high profile with cyber security professionals and data scientists being the language of choice for many. The term “artificial intelligence” (AI) can often be a marketing device but this does not apply in the eBook ‘Hands-On Artificial Intelligence for Cybersecurity’. Author, Dr Alessandro Parisi, illuminates the term ‘AI’ shedding light upon its employment of perceptrons, the single and especially the multi layer which shows the highest degree of accuracy in predictions, and its analogy with the human brain. The text demonstrates that predictive analytics will result in many security tasks (zero-days detection, uncommon network threats detection, etc.) becoming automated, enabling the analyst to engage in the most noteworthy and challenging threats inspection, detection and resolution, leaving the triage phase to the machine. Another user-friendly design feature is the organisation of the text which provides the python expert and the non-python literate professional, such as myself, the ability to flick back & forth and gain immediate value from the important insights shared. The overall strategy is compelling and its guidance value, for data scientists and cyber security professionals, indispensable – a powerful tool in an AI arsenal.

Amazon Verified review

Ago Jun 09, 2022

Dopo attenta lettura posso pronunciarmi rispetto a questo manuale come uno degli esempi di praticità e chiarezza migliori del settore. Lettura consigliata sia per tutti gli appassionati. A+++++++++

Amazon Verified review

Pasquale Sep 14, 2023

Ottimo libro, finalmente un po' di codice in python.

Amazon Verified review

Tamzid Bhuiyan May 19, 2021

i would recommend it to student in collage or university. I just loved it

Amazon Verified review

sipy Jan 01, 2021

Nothing "hands on" about this book - unless you call instructions for installing Anaconda and Jupyter Notebook "hands-on AI for cybersecurity". Other than that, it's a very wordy primer on what Machine Learning is, and how pieces of it might be used by someone. No code, no examples in the book. Needs an editor to rework it, and change the title to something like "Overview of Machine Learning Concepts". No AI, no 'hands-on". If you want a quick but overly-wordy primer on ML concepts, buy it. Otherwise, pass.

Amazon Verified review

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About the author

Parisi

Alessandro Parisi has been an IT professional for over 20 years, acquiring significant experience as a Security Data Scientist, and as an Artificial Intelligence Cybersecurity and Blockchain specialist. He has experience of operating within organizational and decisional contexts characterized by high complexity. Over the years, he has helped companies to adopt Artificial Intelligence and Blockchain DLT technologies as strategic tools in protecting sensitive corporate assets. He holds a Master Degree in Economics and Statistics.

See other products by Parisi

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Hands-On Artificial Intelligence for Cybersecurity: Implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies

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