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

Topological Data Analysis for Malware Detection and Analysis

The advent of internet technologies has ushered in unprecedented opportunities for global communication and the exchange of information. However, it has also introduced a plethora of security threats, notably malware. These malicious software are designed to infiltrate, damage, or disrupt computing systems, often with severe consequences. Traditional malware detection methods have had varying levels of success but have also highlighted the need for more sophisticated approaches. This chapter explores the application of Topological Data Analysis (TDA) in malware detection and analysis, underscoring its potential to enhance cybersecurity measures.

Applying TDA to malware analysis presents a novel, efficient, and robust technique to identify and categorize malware. Unlike conventional analysis methods, which often hinge on known malware signatures or heuristic rules, TDA does not require prior knowledge of the data. Instead...