Book Image

Hands-On Machine Learning for Cybersecurity

By : Soma Halder, Sinan Ozdemir
Book Image

Hands-On Machine Learning for Cybersecurity

By: Soma 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)
Free Chapter
Basics of Machine Learning in Cybersecurity
Using Data Science to Catch Email Fraud and Spam

What this book covers

Chapter 1, Basics of Machine Learning in Cybersecurity, introduces machine learning and its use cases in the cybersecurity domain. We introduce you to the overall architecture for running machine learning modules and go, in great detail, through the different subtopics in the machine learning landscape.

Chapter 2, Time Series Analysis and Ensemble Modeling, covers two important concepts of machine learning: time series analysis and ensemble learning. We will also analyze historic data and compare it with current data to detect deviations from normal activity.

Chapter 3, Segregating Legitimate and Lousy URLs, examines how URLs are used. We will also study malicious URLs and how to detect them, both manually and using machine learning.

Chapter 4, Knocking Down CAPTCHAs, teaches you about the different types of CAPTCHA and their characteristics. We will also see how we can solve CAPTCHAs using artificial intelligence and neural networks.

Chapter 5, Using Data Science to Catch Email Fraud and Spam, familiarizes you with the different types of spam email and how they work. We will also look at a few machine learning algorithms for detecting spam and learn about the different types of fraudulent email.

Chapter 6, Efficient Network Anomaly Detection Using k-means, gets into the various stages of network attacks and how to deal with them. We will also write a simple model that will detect anomalies in the Windows and activity logs.

Chapter 7, Decision Tree- and Context-Based Malicious Event Detection, discusses malware in detail and looks at how malicious data is injected in databases and wireless networks. We will use decision trees for intrusion and malicious URL detection.

Chapter 8, Catching Impersonators and Hackers Red Handed, delves into impersonation and its different types, and also teaches you about Levenshtein distance. We will also learn how to find malicious domain similarity and authorship attribution.

Chapter 9, Changing the Game with TensorFlow, covers all things TensorFlow, from installation and the basics to using it to create a model for intrusion detection.

Chapter 10, Financial Fraud and How Deep Learning Can Mitigate It, explains how we can use machine learning to mitigate fraudulent transactions. We will also see how to handle data imbalance and detect credit card fraud using logistic regression.

Chapter 11, Case Studies, explores using SplashData to perform password analysis on over one million passwords. We will create a model to extract passwords using scikit-learn and machine learning.