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Hands-On Machine Learning for Cybersecurity

Hands-On Machine Learning for Cybersecurity

By : Halder, Sinan Ozdemir
2.7 (6)
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Hands-On Machine Learning for Cybersecurity

Hands-On Machine Learning for Cybersecurity

2.7 (6)
By: Halder, Sinan Ozdemir

Overview of this book

Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems
Table of Contents (13 chapters)
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1
Basics of Machine Learning in Cybersecurity
5
Using Data Science to Catch Email Fraud and Spam

Computation graphs

In TensorFlow, computational operations are interdependent and thus interact with one another. Computational graphs help to track these interdependencies, thus making sense of a complicated functional architecture.

What is a computation graph?

Each node in the TensorFlow graph is a symbolic representation of an operational process. When the data reaches a particular node during the flow, the corresponding operational function associated with the node is performed. The output of the process is fed to the next node as an input.

The primary benefit of graph computation is that it helps to perform optimized computations:

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