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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
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4
Knocking Down CAPTCHAs
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5
Using Data Science to Catch Email Fraud and Spam
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8
Catching Impersonators and Hackers Red Handed
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11
Case Studies
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12
Other Books You May Enjoy
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Use cases for time series

In the Signal processing section, we will discuss the different fields where time series are utilized to extract meaningful information from very large datasets. Be it social media analysis, click stream trends, or system log generations, time series can be used to mine any data that has a similar time-sensitive approach to data collection and storage.

Signal processing

Digital signal processing uses time series analysis to identify a signal from a mixture of noise and signals. Signal processing uses various methods to perform this identification, like smoothing, correlation, convolution, and so on. Time series helps measure deviations from the stationary behaviors of signals. These drifts or deviations...

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