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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

Supervised learning methods

This section describes the concrete supervised learning methods that can be applied in cybersecurity scenarios. We’ll describe where they can be applied, how they work, and what their advantages and disadvantages are. Supervised learning methods are used to solve problems where we have input data (X) and target outputs (Y), which means the training and validation data needs to be labeled with its associated output. In cybersecurity, an example would be data that is labeled as an attack or benign network traffic. The goal is to train the machine learning model so that it can find the pattern that produces the target output.

In other words, we are looking for a mathematical function (f) with a parameter set (W), where Y=f(X, W). For instance, a simple example would be a linear function, Y=WX. Depending on our assumptions, we can use different types of functions, such as linear, polynomial, sine, and so on. During training, the parameters of the function...

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