Hands-On Machine Learning for Cybersecurity

Delve into the world of smart data security using machine learning algorithms and Python librariesKey FeaturesLearn machine learning algorithms and cybersecurity fundamentalsAutomate your daily workflow by applying use cases to different aspects of securityImplement smart machine learning solutions to detect various cybersecurity problemsBook DescriptionOrganizations are increasingly vulnerable to many cybersecurity threats which can lead to significant financial losses, making smart data security more important than ever. In this book, you'll use different tools and techniques to solve a variety of significant problems that exist in the cybersecurity domain.The book begins by introducing you to the basics of machine learning in cybersecurity using Python and its libraries. You will then explore various machine learning domains, such as time series analysis and ensemble modeling. As you progress, you will implement various examples such as building a system to identify malicious URLs, and creating a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of the k-means algorithm to develop a solution for detecting and alerting you about any malicious activity in the network. In addition to this, you'll get up to speed with implementing biometric authentication and fingerprint scanning to validate whether someone is a legitimate user or not. Finally, you will see how you can use TensorFlow for cybersecurity, along with understanding how deep learning is effective for creating models and training systems.By the end of this book, you will have learned how to effectively use the Python ecosystem and machine learning algorithms for cybersecurity.What you will learnGet to grips with how machine learning and Python can be used in complex cyber issuesApply machine learning algorithms with complex datasets to implement cybersecurity conceptsImplement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problemsLearn to speed up a system using Python libraries with NumPy, scikit-learn, and CUDAMitigate cyber crimes by understanding how to combat malware, detect spam, and fight financial fraud Use TensorFlow in the cybersecurity domain and implement real-world examplesWho this book is forThis book is for data scientists, machine learning developers, security researchers, or anyone looking to apply machine learning for computer security. Having some working knowledge of Python programming and familiarity with machine learning and cybersecurity fundamentals will help you get the most out of this book.Table of ContentsBasics of Machine Learning in CybersecurityTime Series Analysis and Ensemble ModelingSegregating Legitimate and Lousy URLsKnocking Down CAPTCHAsUsing Data Science to Catch Email Fraud and SpamEfficient Network Anomaly Detection Using K-MeansDecision Tree and Context-Based Malicious Event DetectionCatching Impersonators and Hackers Red-HandedChange the Game with TensorFlowFinancial Fraud and How Deep Learning Can Mitigate ThemPractical Case Studies in Cybersecurity