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

Future prospects

The future of AI in malware analysis is promising, with several potential advancements on the horizon. Let’s take a look at them.

Improved adversarial defense

The future state of AI in adversarial defense is likely to be characterized by the following trends:

  • Increased use of adversarial training: Adversarial training is becoming increasingly popular in improving the robustness of machine learning models. As this technique becomes more sophisticated, it is likely to become even more effective at defending against adversarial attacks.
  • Development of new adversarial defenses: Researchers are constantly developing new techniques to defend against adversarial attacks. These techniques are likely to become more effective as AI technology continues to advance.
  • Increased use of XAI: XAI is becoming increasingly important for understanding and defending against adversarial attacks. As XAI techniques become more sophisticated, they are likely to...