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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

Evaluating models

Model evaluation in AI and machine learning (ML) models typically relies on the methodology of selecting training, validation, and test sets and loss functions and metrics that are calculated on those datasets. This enables us to have a quantitative way to assess models and optimize them. For instance, we need a way to measure how good our malware detection model is in classifying executables during the training time, validation time, and test time.

Firstly, we’ll describe and compare the most common loss functions used in different types of ML models.

Loss functions

Loss functions are used when training the model to evaluate its performance during training. Furthermore, they are used to compute gradients, which enable us to determine how to change model parameters to gradually improve the performance of the model in each training step. Loss functions can also be used to evaluate models after training.

The following loss functions are the most used...

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Artificial Intelligence for Cybersecurity
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