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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 advanced ML and AI integration in malware detection

In the dynamic realm of cybersecurity, envisioning the future means grasping the ever-evolving nature of threats and the continual adaptation of defensive measures. The inefficacy of traditional signature-based detection systems against the tide of novel malware variations signals an urgent need for change. As we look ahead, the spotlight turns to advanced ML and AI as primary tools for countering these threats.

Beyond signature-based detection

To appreciate where we are heading, it’s vital to understand the constraints of the past. Traditional signature-based systems have rested heavily on a library of identified malware footprints. Yet, the era of static threats is behind us. The ability for malicious actors to effortlessly modify or generate new malware signatures leaves such systems in the dust.

Enter the world of behavior-based detection.

Behavioral analysis – the AI and ML revolution...

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