Get into the world of smart data security using machine learning algorithms and Python libraries Key Features Learn machine learning algorithms and cybersecurity fundamentals Automate your daily workflow by applying use cases to many facets of security Implement smart machine learning solutions to detect various cybersecurity problems Book Description 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 Who this book is for This book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support an ... 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. 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. Things you will learn: Use machine learning algorithms with complex datasets to implement cybersecurity concepts ; 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. -- Back cover The book will allow readers to implement smart solutions to their existing cybersecurity products and effectively build intelligent solutions which cater to the needs of the future. By the end of this book, you will be able to build, apply, and evaluate machine learning algorithms to identify various cybersecurity potential threats.
