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

How TDA creates a multi-dimensional data representation

TDA creates a multi-dimensional representation of the data, making it possible to uncover intrinsic data structures, highlight unusual patterns, and extract significant features that could signify the presence of malware.

Recall that TDA is a powerful tool that leverages the concepts of topology to analyze complex and high-dimensional datasets. It gives us the capacity to simplify and understand the shape of the data, allowing us to discover intrinsic data structures, highlight unusual patterns, and extract significant features.

Data in the real world, particularly in cybersecurity, tends to be multi-dimensional. For instance, when we are analyzing software for potential malware, we might consider features such as the sequence of system calls made, the binary structure, network activity, and more. Each of these features constitutes a dimension, leading to a high-dimensional dataset.

However, making sense of this high...