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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

AI has revolutionized malware data analysis and detection, empowering organizations to combat the ever-evolving threat landscape more effectively. By leveraging AI techniques, such as machine learning and deep learning, organizations can enhance detection accuracy, monitor threats in real time, and improve overall efficiency. However, challenges related to adversarial attacks, data privacy, and interpretability must be addressed to maximize the potential of AI in malware analysis. With ongoing research and development, AI will continue to play a vital role in bolstering cybersecurity defenses against malware attacks in the future.

Having understood the transformative impact and the potential challenges of AI in malware analysis, it’s essential to delve deeper into specific methods that embody this revolution. In the next chapter, we will explore one such method – behavior-based malware data analysis and detection – that moves away from the traditional...

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