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

Persistence homology – filtering noise to find meaningful patterns

TDA and its method of persistent homology provide a groundbreaking approach to cyber threat detection that complements traditional techniques. In a world where threats are constantly evolving and new malware is being developed, the ability to identify and classify potentially harmful software based on its inherent data structure is invaluable.

To better understand the usefulness of this approach, let’s dig deeper into how persistence diagrams, the graphical representation of topological data, can be leveraged to identify benign software and detect novel threats.

As we explained earlier, benign software, when analyzed using persistent homology, typically presents a predictable structure. This might mean tight clusters of data points representing common or routine software activities, fewer loops indicating less intricate interactions, and simpler connections that align with the software’s legitimate...