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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

Malicious data injection within databases

Better know as SQL injections, these attacks manipulate SQL queries, and are hence able to manage data sources residing in different databases. SQL injections basically trick databases to produce an undesired output. These could include privilege escalation by granting all access rights, deleting or dropping entire tables with potential PII data, enabling data ex-filtration by running select * queries, and then dumping entire data in external devices.

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