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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 deeper OS-level integrations in malware detection

The cybersecurity landscape is in a perpetual state of flux. As technology becomes more ingrained in our daily lives, the avenues for malicious cyberattacks expand in tandem. At the core of almost every device we use is the OS, a critical piece of software that manages hardware resources and provides services for computer programs. With the evolving threat matrix, the need to bolster defenses at the very nucleus of our devices – the OS – becomes even more pressing. The future of malware detection, thus, seems inevitably tied to deeper OS-level integrations.

The current state of malware detection

Traditional malware detection methods have revolved around signature-based techniques. These rely on known patterns or “signatures” of malware. When a piece of software or a file matches this signature, it’s flagged as malicious. While effective against known threats, this method struggles...

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