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

What this book covers

Chapter 1, Malware Science Life Cycle Overview, is an introduction to malware’s life cycle stages, challenges, and applicable data science techniques.

Chapter 2, An Overview of the International History of Cyber Malware Impacts, gives a look at the evolving landscape of cyber threats and global countermeasures, emphasizing data science’s role.

Chapter 3, Topological Data Analysis for Malware Detection and Analysis, is an exploration of how topology data analysis can unearth patterns in malware behavior.

Chapter 4, Artificial Intelligence for Malware Data Analysis and Detection, is a discussion on AI’s capabilities for automating malware analysis and detection.

Chapter 5, Behavior-Based Malware Data Analysis and Detection, is an insight into behavior-based analysis techniques for identifying malicious activities.

Chapter 6, The Future State of Malware Data Analysis and Detection, is an examination of upcoming trends and challenges in malware analysis.

Chapter 7, The Future State of Key International Compliance Requirements, is a review of the regulatory landscape and organizational compliance steps for GDPR and CCPA.

Chapter 8, Epilogue – A Harmonious Overture to the Future of Malware Science and Cybersecurity, metaphorically compares the evolving field of malware science to a symphony, emphasizing its role as a proactive and dynamic defense against the ever-changing landscape of cybersecurity threats.