Hands-On Machine Learning for Cybersecurity

Machine Learning is a growing trend in every technological field including computer security. Many research and practical applications are in line which has a potential to change the way how data is secured. With this book, you will stand a chance to mark your developments in cyber security domain using machine learning capabilities.

This book begins with giving you the basics of machine learning in cyber security using python and their extensive libraries support. You will explore various machine learning domains such as time series analysis, ensemble modeling to get your foundations right. You will implement your learning in various examples such as building system to identify malicious URLs, bypass defensive technologies, and build a program for detecting email frauds and spam using supervised learning and Naive Bayes algorithm. Later you will learn to make effective use of K means algorithm, to develop a solution to detect and alert any malicious activity going on the network. Next, you will be building weightless and complex decision tree and you will implement Digital biometrics and fingerprint from users interaction 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 in creating models and training the system from previous fraudulent events so that they can be mitigated in future.

By the end of this book, you will be able to build, apply, and evaluate machine learning algorithms to identify potential threats such as intrusion detection and malware. You will be introduced to cutting-edge big data tools and GPU processing to show how these techniques can be applied to extremely large data sets to detect traffic and end-point behavior.