Hands-On Artificial Intelligence for Cybersecurity
eBook - ePub

Hands-On Artificial Intelligence for Cybersecurity

Implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies

Alessandro Parisi

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  1. 342 pagine
  2. English
  3. ePUB (disponibile sull'app)
  4. Disponibile su iOS e Android
eBook - ePub

Hands-On Artificial Intelligence for Cybersecurity

Implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies

Alessandro Parisi

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Informazioni sul libro

Build smart cybersecurity systems with the power of machine learning and deep learning to protect your corporate assets

Key Features

  • 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

Book 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.

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

Who this book is 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.

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Section 1: AI Core Concepts and Tools of the Trade

In this section, the fundamental concepts of AI will be introduced, including analyzing the different types of algorithms and the most indicated use strategies for cybersecurity.
This section contains the following chapters:
  • Chapter 1, Introduction to AI for Cybersecurity Professionals
  • Chapter 2, Setting Up Your AI for Cybersecurity Arsenal

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 increasingly need to invest in automated analysis tools that enable a rapid and adequate response to current and future cybersecurity challenges. Therefore, the scenario that is looming is actually a combination of skills, rather than a clash between human operators and machines. It is therefore likely that the AI within the field of cybersecurity will take charge of the dirty work, that is, the selection of potential suspect cases, leaving the most advanced tasks to the security analysts, letting them investigate in more depth the threats that deserve the most attention.

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 options were hardcoded within the automated learning solutions, and were verified by experts in the field.
The fundamental limitation of such expert systems consisted of the fact that they reduced the decisions to Boolean values (which reduce everything down to a binary choice), thus limiting the ability to adapt the solutions to the different nuances of real-world use cases.
In fact, expert systems do not learn anything new compared to hardcoded solutions, but limit themselves to looking for the right answer within a (potentially very large) knowledge base that is not able to adapt to new problems that were not addressed previously.

Reflecting the indeterministic nature of reality

Since the concrete cases that we come across in the real world cannot simply be represented using just true/false classification models (although experts in the sector strive to list all possible cases, there is always something in reality that escapes classification), it is therefore necessary to make the best use of the data at our disposal in order to let latent tendencies and anomalous cases (such as outliers) emerge, making use of statistical and probabilistic models that can more appropriately reflect the indeterministic nature of reality.

Going beyond statistics toward machine learning

Although the introduction of statistical models broke through the limitations of expert systems, the underlying rigidity of the approach remained, because statistical models, such as rule-based decisions, were in fact established in advance and could not be modified to adapt to new data. For example, one of the most commonly used statistical models is the Gaussian distribution. The statistician could then decide that the data comes from a Gaussian distribution, and try to estimate the parameters that characterize the hypothetical distribution that best describes the data being analyzed, without taking into consideration alternative models.
To overcome these limits, it was therefore necessary to adopt an iterative approach, which allowed the introduction of machine learning (ML) algorithms capable of generalizing the descriptive models starting from the available data, thus autonomously generating its own features, without limiting itself to predefined target functions, but adapting them to the continuous evolution of the algorithm training process.

Mining data for models

The difference in approach compared to the predefined static models is also reflected in the research field known as data mining.
An adequate definition of the data mining process consists of the discovery of adequate representative models, starting with the data. Also, in this case, instead of adopting pre-established statistical models, we can use ML algorithms based on the training data to identify the most suitable predictive model (this is more true when we are not able to understand the nature of the data at our disposal).
However, the algorithmic approach is not always adequate. When the nature of the data is clear and conforms to known models, there is no advantage in using ML algorithms instead of pre-defined models. The next step, which absorbs and extends the advantages of the previous approaches, adding the ability to manage cases not covered in the training data, leads us to AI.
AI is a wider field of research than ML, which can manage data of a more generic and abstract nature than ML, thus enabling the transfer of common solutions to different types of data without the need for complete retraining. In this way, it is possible, for example, to recognize objects from color images, starting with objects originally obtained from black and white samples.
Therefore, AI is considered as a broad field of research that includes ML. In turn, ML includes deep learning (DL) which is ML method based on artificial neural networks, as shown in the following diagram:

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 ar...

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