Book Image

Malware Science

By : Shane Molinari
Book Image

Malware Science

By: Shane Molinari

Overview of this book

In today's world full of online threats, the complexity of harmful software presents a significant challenge for detection and analysis. This insightful guide will teach you how to apply the principles of data science to online security, acting as both an educational resource and a practical manual for everyday use. Malware Science starts by explaining the nuances of malware, from its lifecycle to its technological aspects before introducing you to the capabilities of data science in malware detection by leveraging machine learning, statistical analytics, and social network analysis. As you progress through the chapters, you’ll explore the analytical methods of reverse engineering, machine language, dynamic scrutiny, and behavioral assessments of malicious software. You’ll also develop an understanding of the evolving cybersecurity compliance landscape with regulations such as GDPR and CCPA, and gain insights into the global efforts in curbing cyber threats. By the end of this book, you’ll have a firm grasp on the modern malware lifecycle and how you can employ data science within cybersecurity to ward off new and evolving threats.
Table of Contents (15 chapters)
1
Part 1– Introduction
Free Chapter
2
Chapter 1: Malware Science Life Cycle Overview
4
Part 2 – The Current State of Key Malware Science AI Technologies
8
Part 3 – The Future State of AI’s Use for Malware Science
11
Chapter 8: Epilogue – A Harmonious Overture to the Future of Malware Science and Cybersecurity
Appendix

Transforming a malware binary into a topological space

Now, let’s expand on how topological analysis of malware begins with transforming the malware binary or behavioral data into a topological space, often a simplicial complex. Topological analysis of malware is a modern approach to cybersecurity that involves converting complex data about malware into a form that can be better understood and studied. This process might sound abstract and complicated, but let’s break it down into more relatable terms using an analogy.

Imagine that you’re looking at a massive crowd of people from a bird’s-eye view. Each person in this crowd can be thought of as a data point. Now, suppose you want to understand more about the people in the crowd – their relationships, groups, and any unusual behavior. Trying to examine each person individually would be an overwhelming task. A much more efficient approach would be to look for patterns within the crowd.

This is...