Hands-On Machine Learning for Cybersecurity
Safeguard your system by making your machines intelligent using the Python ecosystem
Published on 31. December 2018
Book
Paperback/Softback
318 pages
978-1-78899-228-2 (ISBN)
Description
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 DescriptionCyber 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 forThis 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
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 DescriptionCyber 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 forThis 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
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 18 mm
Weight
597 gr
ISBN-13
978-1-78899-228-2 (9781788992282)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Soma Halder | Sinan Ozdemir
Hands-On Machine Learning for Cybersecurity
Safeguard your system by making your machines intelligent using the Python ecosystem
E-Book
06/2024
1st Edition
Packt Publishing Limited
€41.49
Available for download
Persons
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 Ozdemir is a data scientist, start-up founder, and educator living in the San Francisco Bay Area. He studied pure mathematics at the Johns Hopkins University. He then spent several years conducting lectures on data science there, before founding his own start-up, Kylie ai, which uses artificial intelligence to clone brand personalities and automate customer service communications. He is also the author of Principles of Data Science, available through Packt.
Content
Table of Contents
Basics of Machine Learning in Cyber Security
Time series analysis and Ensemble modelling
Segregating legitimate and lousy URLs
Knocking down captchas
Using Data Science to catch email frauds and spams
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 frauds and how deep learning can mitigate them
Practical Case Studies in Cyber Security
Basics of Machine Learning in Cyber Security
Time series analysis and Ensemble modelling
Segregating legitimate and lousy URLs
Knocking down captchas
Using Data Science to catch email frauds and spams
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 frauds and how deep learning can mitigate them
Practical Case Studies in Cyber Security