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

Normalcy and anomaly detection

In cybersecurity, the concepts of normalcy and anomaly detection are fundamental for establishing robust protective mechanisms. Normalcy refers to the expected, routine behaviors and operations within a system or network. This understanding of normalcy acts as a reference point for identifying irregularities or suspicious activities. However, the notion of what is considered “normal” is dynamic; it evolves with changes in system configurations, network traffic, and user behaviors. On the other hand, anomaly detection aims to identify deviations from this baseline of normalcy, often indicative of potential security threats such as malware or unauthorized intrusions. Operationalizing anomaly detection involves several key steps: establishing a baseline of normal behavior, continuous monitoring of systems or networks, identifying anomalies, investigating these irregularities for potential threats, initiating appropriate responses if threats...