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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

Time series trends and seasonal spikes

Time series analysis can be used to detect attack attempts, like failed logins, using a time series model. Plotting login attempts identifies spikes (/) in failed logins. Such spikes are indicative of account takeover (ATO).

Time series identify another cyber security use case—data exfiltration is the process in which the unauthorized transfer of data takes place from a computer system to a malicious location. Time series can identify huge network data packets being transported out of the network. Data exfiltration could be because of either an outsider compromise or an insider threat. In a later section of the chapter, we will use ensemble learning methods to identify the source of the attack.

We will learn the details of the attack in the next section. The goal of this chapter is to be able to detect reconnaissance so that we are...

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