Book Image

Malware Science

By : Shane Molinari
Book Image

Malware Science

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. Malware Science 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 (15 chapters)
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
Appendix

AI techniques used in malware data analysis

AI techniques used in malware data analysis, such as machine learning and deep learning, have revolutionized malware data analysis. These techniques enable the development of models capable of automatically learning patterns and features from large datasets, facilitating the detection and classification of malware samples. By leveraging AI, security analysts can automate the analysis process, reduce human error, and enhance the overall efficiency and accuracy of malware detection.

Machine learning techniques are widely applied in malware detection and analysis. Machine learning models learn patterns and features from large datasets to identify and classify malware samples. Key machine learning techniques that are utilized in malware analysis include supervised learning, unsupervised learning, and deep learning.

Supervised learning

Supervised learning involves training machine learning models on labeled datasets, where each sample...