Book Image

10 Machine Learning Blueprints You Should Know for Cybersecurity

By : Rajvardhan Oak
4 (1)
Book Image

10 Machine Learning Blueprints You Should Know for Cybersecurity

4 (1)
By: Rajvardhan Oak

Overview of this book

Machine learning in security is harder than other domains because of the changing nature and abilities of adversaries, high stakes, and a lack of ground-truth data. This book will prepare machine learning practitioners to effectively handle tasks in the challenging yet exciting cybersecurity space. The book begins by helping you understand how advanced ML algorithms work and shows you practical examples of how they can be applied to security-specific problems with Python – by using open source datasets or instructing you to create your own. In one exercise, you’ll also use GPT 3.5, the secret sauce behind ChatGPT, to generate an artificial dataset of fabricated news. Later, you’ll find out how to apply the expert knowledge and human-in-the-loop decision-making that is necessary in the cybersecurity space. This book is designed to address the lack of proper resources available for individuals interested in transitioning into a data scientist role in cybersecurity. It concludes with case studies, interview questions, and blueprints for four projects that you can use to enhance your portfolio. By the end of this book, you’ll be able to apply machine learning algorithms to detect malware, fake news, deep fakes, and more, along with implementing privacy-preserving machine learning techniques such as differentially private ML.
Table of Contents (15 chapters)

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system.”

A block of code is set as follows:

import pandas as pd
import numpy as np
import os
from requests import get
}

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

test_input_fn_a = bert.run_classifier.input_fn_builder(
    features=test_features_a,
    seq_length=MAX_SEQ_LENGTH,
    is_training=False,
    drop_remainder=False)

Any command-line input or output is written as follows:

pip install –r requirements.txt

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “The last column, named target, identifies the kind of network attack for every row in the data.”

Tips or important notes

Appear like this.