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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

advanced AI integration

in malware detection 139

advanced ML integration

in malware detection 139

advanced persistent threats (APTs) 68, 107, 141

adversarial attacks 91, 92

adversarial defense

improving 100

adware 4

AI-based malware analysis

challenges 91

AI-based malware analysis, challenges

adversarial attacks 91, 92

data privacy and ethical considerations 93, 94

interpretability and explainability 94, 95

AI ethics and governance standards

future state 162, 163

AI in malware analysis

advancements 100

AI in malware analysis, advancements

adversarial defense, improving 100

hybrid approaches 101, 102

XAI 102

AI, in malware detection

benefits 95-100

AI integration

challenges and considerations 147

defining 146

transformative...