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Data Science for Malware Analysis
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Data Science for Malware Analysis
By:
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.
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.
Table of Contents (14 chapters)
Preface
Part 1– Introduction
Chapter 1: Malware Science Life Cycle Overview
Chapter 2: An Overview of the International History of Cyber Malware Impacts
Part 2 – The Current State of Key Malware Science AI Technologies
Chapter 3: Topological Data Analysis for Malware Detection and Analysis
Chapter 4: Artificial Intelligence for Malware Data Analysis and Detection
Chapter 5: Behavior-Based Malware Data Analysis and Detection
Part 3 – The Future State of AI’s Use for Malware Science
Chapter 6: The Future State of Malware Data Analysis and Detection
Chapter 7: The Future State of Key International Compliance Requirements
Chapter 8: Epilogue – A Harmonious Overture to the Future of Malware Science and Cybersecurity
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Appendix: Index
Customer Reviews