Book Image

Hands-On Artificial Intelligence for Cybersecurity

By : Alessandro Parisi
Book Image

Hands-On Artificial Intelligence for Cybersecurity

By: Alessandro Parisi

Overview of this book

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.
Table of Contents (16 chapters)
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Section 1: AI Core Concepts and Tools of the Trade
Section 2: Detecting Cybersecurity Threats with AI
Section 3: Protecting Sensitive Information and Assets
Section 4: Evaluating and Testing Your AI Arsenal

Using cross validation for algorithms

The type of cross validation most commonly used is known as k-folds cross validation, and it involves randomly dividing the sample dataset into a number of folds, k corresponding to equal portions (if possible) of data.

The learning process is performed in an iterative way, based on the different compositions of folds, used both as a training dataset and as a testing dataset. In this way, each fold is used in turn as a training dataset or as a testing dataset:

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In practice, the different folds (randomly generated) alternate in the role of training and testing datasets, and the iterative process ends when all the k-folds have been used both as training and testing datasets.

Since, at each iteration, the generalization error generated is different (as the algorithm is trained and tested with different...