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Data Science for Malware Analysis

Data Science for Malware Analysis

By : Shane Molinari
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Data Science for Malware Analysis

Data Science for Malware Analysis

4 (4)
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. 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)
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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

Improving detection algorithms to predict the behavior of new malware

Persistent homology, a concept from TDA, offers a novel perspective in dealing with the constant threats posed by malicious software, known as malware. Its unique value lies in its ability to extract significant patterns and structures in complex data across multiple scales. By identifying these so-called persistent features, cybersecurity professionals gain insights into the core structure and behavior of malware, enabling them to enhance detection algorithms and predict the behavior of new or unknown malware strains. Let’s explore this concept more deeply using a practical analogy.

Consider a game of chess. Each player maneuvers their pieces, trying to anticipate the opponent’s moves and strategize accordingly. Skilled chess players often recognize patterns in their opponent’s moves. They can distinguish a defensive player from an aggressive one, or identify specific strategies based on...

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