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

Behavior-based malware data analysis

Behavior-based malware data analysis is a proactive approach to cybersecurity that focuses on the actions that are performed by a piece of software rather than its static attributes, such as its code signature. This shift in focus enables us to detect previously unknown or evolved threats that might not have a known signature but exhibit malicious behavior. The approach can be divided into two main stages:

  • Data collection
  • Behavior analysis

Let’s take a closer look.

Data collection

In this stage, software behavior is monitored and recorded. This can be done through various methods, such as system call tracing, API function call tracking, memory and CPU usage monitoring, network traffic analysis, and more. The objective is to capture as much relevant behavior data as possible without overly impacting system performance.

Behavior analysis

This is where the collected data is analyzed to identify potential malicious...