Book Image

Machine Learning for Cybersecurity Cookbook

By : Emmanuel Tsukerman
Book Image

Machine Learning for Cybersecurity Cookbook

By: Emmanuel Tsukerman

Overview of this book

Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach.
Table of Contents (11 chapters)

Training an XGBoost classifier

Gradient boosting is widely considered the most reliable and accurate algorithm for generic machine learning problems. We will utilize XGBoost to create malware detectors in future recipes.

Getting ready

The preparation for this recipe consists of installing the scikit-learn, pandas, and xgboost packages in pip. The command for this is as follows:

pip install sklearn xgboost pandas

In addition, a dataset named file_pe_header.csv is provided in the repository for this recipe.

How to do it...

In the following steps, we will demonstrate how to instantiate, train, and test an XGBoost classifier:

  1. Start by reading in the data:
import pandas as pd

df = pd.read_csv("file_pe_headers.csv", sep=",")
y = df["Malware"]
X = df.drop(["Name", "Malware"], axis=1).to_numpy()
  1. Next, train-test-split a dataset:
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
  1. Create one instance of an XGBoost model and train it on the training set:
from xgboost import XGBClassifier

XGB_model_instance = XGBClassifier(), y_train)
  1. Finally, assess its performance on the testing set:
from sklearn.metrics import accuracy_score

y_test_pred = XGB_model_instance.predict(X_test)
accuracy = accuracy_score(y_test, y_test_pred)
print("Accuracy: %.2f%%" % (accuracy * 100))

The following screenshot shows the output:

How it works...

We begin by reading in our data (step 1). We then create a train-test split (step 2). We proceed to instantiate an XGBoost classifier with default parameters and fit it to our training set (step 3). Finally, in step 4, we use our XGBoost classifier to predict on the testing set. We then produce the measured accuracy of our XGBoost model's predictions.