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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

Using malware persistence diagrams to classify unknown software

Cybersecurity experts leverage a variety of approaches to detect and counter malware threats. One of these approaches is the use of signatures or known patterns of behavior that are indicative of a specific malware. However, modern malware employs sophisticated techniques to evade such signature-based detection methods. This is where TDA and its associated method of persistent homology can provide a significant edge.

To further expand on the example given: persistent homology creates a topological summary of high-dimensional data in the form of a persistence diagram. This diagram shows the “birth” and “death” of topological features, such as clusters and loops, as we vary the scale. By observing these diagrams, we can identify certain recurring patterns or “persistent features” that are commonly seen in the persistence diagrams of known malware.

Take, for instance, a certain...

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