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)
Free Chapter
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

How to classify network attacks

We have seen that it is possible to use all different types of algorithms (such as supervised, unsupervised, and reinforcement learning), even in the implementation of network anomaly detection systems.

But how can we effectively train these algorithms in order to identify the anomalous traffic?

It will be necessary to first identify a training dataset that is representative of the traffic considered normal within a given organization.

To this end, we will have to adequately choose the representative features of our model.

The choice of features is of particular importance, as they provide a contextual value to the analyzed data, and consequently determine the reliability and accuracy of our detection system.

In fact, choosing features that are not characterized by high correlation with possible anomalous behaviors translates into high error rates...