Book Image

Hands-On Machine Learning for Cybersecurity

By : Soma Halder, Sinan Ozdemir
Book Image

Hands-On Machine Learning for Cybersecurity

By: Soma Halder, Sinan Ozdemir

Overview of this book

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
Table of Contents (13 chapters)
Free Chapter
Basics of Machine Learning in Cybersecurity
Using Data Science to Catch Email Fraud and Spam

SVM to detect malicious URLs

We will now use another machine learning approach to detect malicious URLs. Support vector machines (SVMs) are a popular method for classifying whether a URL is malicious or benign.

An SVM model classifies data across two or more hyperplanes. The output of the model is a hyperplane that can be used to segregate the input dataset, as shown in the following graph:

We then import the required packages. The SVM package available in the sklearn package (as shown in the following code) is very useful for this purpose:

#use SVM
from sklearn.svm import SVC
svmModel = SVC(), y_train)
#lsvcModel =, y_train)
svmModel.score(X_test, y_test)

Once the model is trained with the SVM classifier, we will again load the model and the feature vector to predict the URL's nature using the model, as shown in the following code...