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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

A deeper dive into the “shape of the data”

The concept of shape in topological data analysis is quite different from how we traditionally understand shapes in geometry. Instead of focusing on rigid properties such as lengths, angles, and areas, the shape in topology refers to the broader, more flexible structure of data. It looks at how data points relate to each other and form a larger pattern or structure.

Imagine that you have a cluster of data points. At the simplest level, you could look at the points individually. However, this wouldn’t provide much insight beyond each point’s specific characteristics. In contrast, topological data analysis allows you to take a step back and view the dataset as a whole.

To visualize this concept, let’s consider a simple example. Suppose you have a dataset comprising various species of animals recorded from different habitats. The data includes attributes such as size, diet, habitat type, and other traits...

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