Title: Hands-On Machine Learning for Cybersecurity: Safeguard your system by making your machines intelligent using the Python ecosystem
Publisher: Packt Publishing
Publication Date: 2018
Language: English
ISBN 10: 1788992288
ISBN 13: 9781788992282
Binding: Paperback
Condition: Very Good
Weight: 599 grams

About this title

Synopsis

Delve 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 different aspects of security
Implement smart machine learning solutions to detect various cybersecurity problems

Book Description

Organizations 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 learn

Get to grips with how machine learning and Python can be used in complex cyber issues
Apply 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
Mitigate cyber crimes by understanding how to combat malware, detect spam, and fight financial fraud
Use TensorFlow in the cybersecurity domain and implement real-world examples

Who this book is for

This 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.

Basics of Machine Learning in Cybersecurity
Time Series Analysis and Ensemble Modeling
Segregating Legitimate and Lousy URLs
Knocking Down CAPTCHAs
Using Data Science to Catch Email Fraud and Spam
Efficient Network Anomaly Detection Using K-Means
Decision Tree and Context-Based Malicious Event Detection
Catching Impersonators and Hackers Red-Handed
Change the Game with TensorFlow
Financial Fraud and How Deep Learning Can Mitigate Them
Practical Case Studies in Cybersecurity

About the Authors

Soma Halder is the data science lead of the big data analytics group at Reliance Jio Infocomm Ltd, one of India's largest telecom companies. She specializes in analytics, big data, cybersecurity, and machine learning. She has approximately 10 years of machine learning experience, especially in the field of cybersecurity. She studied at the University of Alabama, Birmingham where she did her master's with an emphasis on Knowledge discovery and Data Mining and computer forensics. She has worked for Visa, Salesforce, and AT&T. She has also worked for start-ups, both in India and the US (E8 Security, Headway ai, and Norah ai). She has several conference publications to her name in the field of cybersecurity, machine learning, and deep learning.

Sinan is an active lecturer focusing on large language models and a former lecturer of data science at the Johns Hopkins University. He is the author of multiple textbooks on data science and machine learning including "Quick Start Guide to LLMs". Sinan is currently the founder of LoopGenius which uses AI to help people and businesses boost their sales and was previously the founder of the acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a Master's Degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco.

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