Hands-On Machine Learning for Cybersecurity: Safeguard your system by making your machines intelligent using the Python ecosystem
Authors: Soma Halder – Sinan Ozdemir
ISBN-10: 1788992288
ISBN-13: 9781788992282
Released: 2018-12-31
Print Length 页数: 318 pages
Book Description
Get into the world of smart data security using machine learning algorithms and Python libraries
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
What you will learn
Use machine learning algorithms with complex datasets to implement cybersecurity concepts
Implement machine learning algorithms such as clustering,k-means,and Naive Bayes to solve real-world problems
Learn to speed up a system using Python libraries with NumPy,Scikit-learn,and CUDA
Understand how to combat malware,detect spam,and fight financial fraud to mitigate cyber crimes
Use TensorFlow in the cybersecurity domain and implement real-world examples
Learn how machine learning and Python can be used in complex cyber issues
Contributors
Contents
Preface
Chapter 1: Basics of Machine Learning in Cybersecurity
Chapter 2: Time Series Analysis and Ensemble Modeling
Chapter 3: Segregating Legitimate and Lousy URLs
Chapter 4: Knocking Down CAPTCHAs
Chapter 5: Using Data Science to Catch Email Fraud and Spam
Chapter 6: Efficient Network Anomaly Detection Using k-means
Chapter 7: Decision Tree and Context-Based Malicious Event Detection
Chapter 8: Catching Impersonators and Hackers Red Handed
Chapter 9: Changing the Game with TensorFlow
Chapter 10: Financial Fraud and How Deep Learning Can Mitigate It
Chapter 11: Case Studies
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Index
