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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

Artificial Intelligence for Malware Data Analysis and Detection

The increasing sophistication and frequency of malware attacks pose significant challenges to cybersecurity professionals. Traditional methods of detecting and analyzing malware are often unable to keep up with the evolving threat landscape. Artificial intelligence (AI) has emerged as a powerful tool for malware data analysis and detection, providing more accurate and efficient solutions to combat this growing problem.

This chapter provides an in-depth exploration of AI techniques that are used in malware data analysis, including machine learning and deep learning approaches. This chapter explores the role of AI in malware analysis, its benefits, challenges, and future prospects.

We will cover the following topics in this chapter:

  • AI techniques used in malware data analysis
  • Benefits of AI in malware detection
  • Future prospects

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