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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

The future state of automated malware analysis

In the continuously mutating landscape of cybersecurity, the surge of new malware poses daunting challenges. Each day heralds the arrival of numerous malware samples, each with potentially unique functionalities, signatures, and attack vectors. Manual malware analysis, with its time-consuming and intricate processes, struggles to keep pace. The future, therefore, appears to rest on the pillars of automation.

Why manual processes are no longer viable

Traditional manual malware analysis entailed a meticulous dissection of malicious software. Analysts would study its behavior, ascertain its functionalities, and investigate the underlying code to comprehend its mechanisms. But the exponential rise in malware, buoyed by automated malware generation tools, means that manual methods are akin to using a bucket to empty an overflowing river. Let’s look at why manual processes are no longer viable:

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