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  • Book Overview & Buying Artificial Intelligence for Cybersecurity
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Artificial Intelligence for Cybersecurity

Artificial Intelligence for Cybersecurity

By : Bojan Kolosnjaji, Huang Xiao, Peng Xu, Apostolis Zarras
4.3 (4)
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Artificial Intelligence for Cybersecurity

Artificial Intelligence for Cybersecurity

4.3 (4)
By: Bojan Kolosnjaji, Huang Xiao, Peng Xu, Apostolis Zarras

Overview of this book

Artificial intelligence offers data analytics methods that enable us to efficiently recognize patterns in large-scale data. These methods can be applied to various cybersecurity problems, from authentication and the detection of various types of cyberattacks in computer networks to the analysis of malicious executables. Written by a machine learning expert, this book introduces you to the data analytics environment in cybersecurity and shows you where AI methods will fit in your cybersecurity projects. The chapters share an in-depth explanation of the AI methods along with tools that can be used to apply these methods, as well as design and implement AI solutions. You’ll also examine various cybersecurity scenarios where AI methods are applicable, including exercises and code examples that’ll help you effectively apply AI to work on cybersecurity challenges. The book also discusses common pitfalls from real-world applications of AI in cybersecurity issues and teaches you how to tackle them. By the end of this book, you’ll be able to not only recognize where AI methods can be applied, but also design and execute efficient solutions using AI methods. *Email sign-up and proof of purchase required
Table of Contents (27 chapters)
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1
Part 1: Data-Driven Cybersecurity and AI
5
Part 2: AI and Where It Fits In
9
Part 3: Applications of AI in Cybersecurity
17
Part 4: Common Problems When Applying AI in Cybersecurity
23
Part 5: Final Remarks and Takeaways

Unsupervised learning methods

In unsupervised learning, the task is to train a model that represents the input data, without having the target data available. This section will help you understand the most prominent unsupervised learning methods and give you an idea of when they are useful in cybersecurity scenarios.

Typically, the learned model in unsupervised learning provides a compressed representation of the input data that abstracts away the noise and uncovers the latent structure in the dataset. For instance, in clustering, the input data is represented by the clusters that have been uncovered by the clustering model. The parameters of the model govern the mapping between the input data and the cluster IDs.

The following figure illustrates the underlying structure of input data. In this case, the data can be naturally grouped into three clusters:

Figure 5.12  – Example of clusters in data

Figure 5.12 – Example of clusters in data

In the following subsection, we’ll describe...

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Artificial Intelligence for Cybersecurity
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