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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

Summary

In this chapter, we explored various facets of TDA and its applicability in the domain of cybersecurity, particularly for malware detection. The discussion ranged from understanding the foundational principles of topology and its relevance in data analysis to diving into specialized topics such as persistence homology. We also touched on the benefits of employing TDA in AI systems for recognizing evolving cyber threats and how these advanced techniques can contribute to the ongoing battle against malware.

One of the key themes that we highlighted was the adaptability and robustness of TDA in filtering out noise and distinguishing meaningful patterns in complex datasets. This ability is especially crucial for detecting zero-day threats and classifying malware into different types or families based on their persistent features. The concept of classification as a nuanced approach, not just for labeling but also for understanding the threat landscape, was emphasized as well...

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