Author: Soma Halder
Pub Date: 2018
Size: 43 Mb
Get into the world of smart data security using machine learning algorithms and Python libraries
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