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Hands-On Artificial Intelligence for Cybersecurity
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

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Hands-On Artificial Intelligence for Cybersecurity

Introduction to AI for Cybersecurity Professionals

In this chapter, we'll distinguish between the various branches of Artificial Intelligence (AI), focusing on the pros and cons of the different approaches of automated learning in the field of cybersecurity.

We will introduce different strategies for learning and optimizing of the various algorithms, and we'll also look at the main concepts of AI in action using Jupyter Notebooks and the scikit-learn Python library.

This chapter will cover the following topics:

  • Applying AI in cybersecurity
  • The evolution from expert systems to data mining and AI
  • The different forms of automated learning
  • The characteristics of algorithm training and optimization
  • Beginning with AI via Jupyter Notebooks
  • Introducing AI in the context of cybersecurity

Applying AI in cybersecurity

The application of AI to cybersecurity is an experimental research area that's not without problems, which we will try to explain during this chapter. However, it is undeniable that the results achieved so far are promising, and that in the near future the methods of analysis will become common practice, with clear and positive consequences in the cybersecurity professional field, both in terms of new job opportunities and new challenges.

When dealing with the topic of applying AI to cybersecurity, the reactions from insiders are often ambivalent. In fact, reactions of skepticism alternate with conservative attitudes, partly caused by the fear that machines will supplant human operators, despite the high technical and professional skills of humans, acquired from years of hard work.

However, in the near future, companies and organizations will...

Evolution in AI: from expert systems to data mining

To understand the advantages associated with the adoption of AI in the field of cybersecurity, it is necessary to introduce the underlying logic to the different methodological approaches that characterize AI.

We will start with a brief historical analysis of the evolution of AI in order to fully evaluate the potential benefits of applying it in the field of cybersecurity.

A brief introduction to expert systems

One of the first attempts at automated learning consisted of defining the rule-based decision system applied to a given application domain, covering all the possible ramifications and concrete cases that could be found in the real world. In this way, all the possible...

Types of machine learning

The process of mechanical learning from data can take different forms, with different characteristics and predictive abilities.

In the case of ML (which, as we have seen, is a branch of research belonging to AI), it is common to distinguish between the following types of ML:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

The differences between these learning modalities are attributable to the type of result (output) that we intend to achieve, based on the nature of the input required to produce it.

Supervised learning

In the case of supervised learning, algorithm training is conducted using an input dataset, from which the type of output that we have to obtain is already known...

Algorithm training and optimization

When preparing automated learning procedures, we will often face a series of challenges. We need to overcome these challenges in order to recognize and avoid compromising the reliability of the procedures themselves, thus preventing the possibility of drawing erroneous or hasty conclusions that, in the context of cybersecurity, can have devastating consequences.

One of the main problems that we often face, especially in the case of the configuration of threat detection procedures, is the management of false positives; that is, cases detected by the algorithm and classified as potential threats, which in reality are not. We will discuss false positives and ML evaluation metrics in more depth in Chapter 7, Fraud Prevention with Cloud AI Solutions, and Chapter 9, Evaluating Algorithms.

The management of false positives is particularly burdensome...

Getting to know Python's libraries

In the following sections, we will explore the concepts presented so far, presenting some sample code that make use of a series of Python libraries that are among the most well known and widespread in the field of ML:

  • NumPy (version 1.13.3)
  • pandas (version 0.20.3)
  • Matplotlib (version 2.0.2)
  • scikit-learn (version 0.20.0)
  • Seaborn (version 0.8.0)

The sample code will be shown here in the form of snippets, along with screenshots representing their output. Do not worry if not all of the implementation details are clear to you at first glance; we will have the opportunity to understand the implementation aspects of every single algorithm throughout the book.

Supervised learning example – linear regression

...

AI in the context of cybersecurity

With the exponential increase in the spread of threats associated with the daily diffusion of new malware, it is practically impossible to think of dealing effectively with these threats using only analysis conducted by human operators. It is necessary to introduce algorithms that allow us to automate that introductory phase of analysis known as triage, that is to say, to conduct a preliminary screening of the threats to be submitted to the attention of the cybersecurity professionals, allowing us to respond in a timely and effective manner to ongoing attacks.

We need to be able to respond in a dynamic fashion, adapting to the changes in the context related to the presence of unprecedented threats. This implies not only that the analysts manage the tools and methods of cybersecurity, but that they can also correctly interpret and evaluate the...

Summary

In this chapter, we have introduced the fundamental concepts of AI and ML in relation to the context of cybersecurity. We have presented some of the strategies adopted in the management of automated learning process, and the possible problems that data analysts face. The concepts and tools that we have learned in this chapter will be used and adapted in the following chapters, addressing the specific problems of cybersecurity.

In the next chapter, we will learn how to manage Jupyter interactive notebooks in more depth, which allows the reader to interactively execute the instructions given and display the results of the execution in real time.

During the course of the book, the concepts of AI and ML will be presented from time to time in the topics covered in the individual chapters, trying to provide a practical interpretation of the algorithms examined. For those interested...

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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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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 02, 2019
Length: 342 pages
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Language : English
ISBN-13 : 9781789804027
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Product Details

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Length: 342 pages
Edition : 1st
Language : English
ISBN-13 : 9781789804027
Category :
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Table of Contents

15 Chapters
Section 1: AI Core Concepts and Tools of the Trade Chevron down icon Chevron up icon
Introduction to AI for Cybersecurity Professionals Chevron down icon Chevron up icon
Setting Up Your AI for Cybersecurity Arsenal Chevron down icon Chevron up icon
Section 2: Detecting Cybersecurity Threats with AI Chevron down icon Chevron up icon
Ham or Spam? Detecting Email Cybersecurity Threats with AI Chevron down icon Chevron up icon
Malware Threat Detection Chevron down icon Chevron up icon
Network Anomaly Detection with AI Chevron down icon Chevron up icon
Section 3: Protecting Sensitive Information and Assets Chevron down icon Chevron up icon
Securing User Authentication Chevron down icon Chevron up icon
Fraud Prevention with Cloud AI Solutions Chevron down icon Chevron up icon
GANs - Attacks and Defenses Chevron down icon Chevron up icon
Section 4: Evaluating and Testing Your AI Arsenal Chevron down icon Chevron up icon
Evaluating Algorithms Chevron down icon Chevron up icon
Assessing your AI Arsenal Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(5 Ratings)
5 star 80%
4 star 0%
3 star 0%
2 star 20%
1 star 0%
Pasquale Sep 14, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Ottimo libro, finalmente un po' di codice in python.
Amazon Verified review Amazon
Ago Jun 09, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
Tamzid Bhuiyan May 19, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
i would recommend it to student in collage or university. I just loved it
Amazon Verified review Amazon
sipy Jan 01, 2021
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
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 Amazon
Jan McSweeney Dec 01, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
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