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

Overcoming the increased complexity of evolving cyber threats

Addressing the future challenges of increased complexity, data volume, privacy regulations, and evolving cyber threats in the realm of normalcy and anomaly detection will require multi-pronged strategies encompassing technology innovation, regulatory compliance, and cybersecurity best practices. In this section, we will look at some possible ways to overcome these challenges:

  • Handling increased complexity and data volume
  • Navigating privacy regulations
  • Mitigating evolving cyber threats

Let’s get started.

Handling increased complexity and data volume

Let us look at ways to overcome this challenge.

  • Advanced machine learning and AI: To handle the increased complexity and data volume, the use of more sophisticated machine learning and AI models will be essential. These models can effectively analyze large datasets and complex relationships. Techniques such as deep learning and reinforcement...