Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Machine Learning for Cybersecurity

You're reading from   Hands-On Machine Learning for Cybersecurity Safeguard your system by making your machines intelligent using the Python ecosystem

Arrow left icon
Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781788992282
Length 318 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Soma Halder Soma Halder
Author Profile Icon Soma Halder
Soma Halder
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Basics of Machine Learning in Cybersecurity 2. Time Series Analysis and Ensemble Modeling FREE CHAPTER 3. Segregating Legitimate and Lousy URLs 4. Knocking Down CAPTCHAs 5. Using Data Science to Catch Email Fraud and Spam 6. Efficient Network Anomaly Detection Using k-means 7. Decision Tree and Context-Based Malicious Event Detection 8. Catching Impersonators and Hackers Red Handed 9. Changing the Game with TensorFlow 10. Financial Fraud and How Deep Learning Can Mitigate It 11. Case Studies 12. Other Books You May Enjoy

Levenshtein distance

Levenshtein distance is an editing distance-based metric that helps to detect the distance between two alphanumeric string sequences. It computes the number of edits (replacements or insertions) required to traverse from the first character sequence to the second character sequence.

The Levenshtein distance between two alphanumeric sequences a and b can be computed as follows:

Where is the indicator function equal to 0 when , and equal to 1 otherwise.

We will now see examples based on Levenshtein distance.

Finding domain similarity between malicious URLs

The following code is a Python-based implementation of the iterative Levenshtein distance:

def iterative_levenshtein(a, b):  
rows = len(a)+1 cols...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image