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

Leveraging classification to manage threat response

The domain of cybersecurity presents a unique challenge, characterized by a continual need to adapt to evolving threats. Each malware sample represents an ongoing effort by malicious actors to subvert digital systems. Understanding these threats at a deeper level can be the key to crafting effective defenses and neutralizing them. This is where TDA comes into play, offering an advanced methodology to classify and comprehend these threats.

In the context of malware analysis, classification is more than just about assigning labels to unknown samples. It’s about understanding the fundamental nature of the threat. This is where TDA, and particularly persistent homology, can offer profound insights. When we classify malware using persistent homology, we’re not simply assigning it into a category based on a shallow comparison of signatures. Instead, we’re delving deeper, examining the topological shape of the data...