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

Homology

Recall that I mentioned TDA’s strength lies in its persistence homology – a tool that identifies and quantifies topological features at various scales. Persistence homology is one of the most powerful tools in the TDA toolbox. To explain it simply, let’s use the analogy of taking photographs of a mountain range at different altitudes.

Imagine you’re in a helicopter, ascending from the base to the peak of a mountain range. As you ascend, you take a series of photographs. At the base, you capture individual mountains. As you rise higher, you start to see groups of mountains and then entire sections of the mountain range. By the time you’re at the peak, you have a complete, bird’s-eye view of the range.

In this analogy, each photograph you take represents a scale. Just like how different scales reveal different details about the mountain range, persistence homology explores data at different scales to uncover various topological...