Malware Science

Unlock the secrets of malware data science with cutting-edge techniques, AI-driven analysis, and international compliance standards to stay ahead of the ever-evolving cyber threat landscape

Key Features:

- Get introduced to three primary AI tactics used in malware and detection

- Leverage data science tools to combat critical cyber threats

- Understand regulatory requirements for using AI in cyber threat management

- Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

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.

Data Science for Malware Analysis 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.

What You Will Learn:

- Understand the science behind malware data and its management lifecycle

- Explore anomaly detection with signature and heuristics-based methods

- Analyze data to uncover relationships between data points and create a network graph

- Discover methods for reverse engineering and analyzing malware

- Use ML, advanced analytics, and data mining in malware data analysis and detection

- Explore practical insights and the future state of AI's use for malware data science

- Understand how NLP AI employs algorithms to analyze text for malware detection

Who this book is for:

This book is for cybersecurity experts keen on adopting data-driven defense methods. Data scientists will learn how to apply their skill set to address critical security issues, and compliance officers navigating global regulations like GDPR and CCPA will gain indispensable insights. Academic researchers exploring the intersection of data science and cybersecurity, IT decision-makers overseeing organizational strategy, and tech enthusiasts eager to understand modern cybersecurity will also find plenty of useful information in this guide. A basic understanding of cybersecurity and information technology is a prerequisite.

Table of Contents

- Malware Data Science Life Cycle Overview

- An Overview of the International History of Cyber Malware Impacts

- Topological Data Analysis for Malware Detection and Analysis

- Artificial Intelligence for Malware Data Analysis and Detection

- Behavior-Based Malware Data Analysis and Detection

- The Future State of Malware Data Analysis and Detection

- The Future State of Key International Compliance Requirements

- Epilogue - A Harmonious Overture to the Future of Malware Data Science and Cybersecurity