Malware Science : A Comprehensive Guide to Detection, Analysis, and Compliance / Shane Molinari and Jim Packer.

Format:

Book

Author/Creator:

Molinari, Shane, author.

Packer, Jim, author.

Language:

English

Subjects (All):

Malware (Computer software).

Computer security.

Physical Description:

1 online resource (230 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2023]

Biography/History:

Molinari Shane: Shane Molinari is a cyber-threat management veteran with over 20 years of experience in military, Department of Defense, and civilian sectors. As an authority in the cyber-risk industry, he brings a nuanced understanding of data science's role in combating malware. Shane holds degrees focusing on Engineering and Systems Design, respectively. A Certified Information Systems Security Professional (CISSP), he's penned thought leadership articles and has been a featured speaker in cybersecurity podcasts. His diverse experience spans from implementing business continuity programs to advising Fortune 500 companies on navigating data privacy regulations like GDPR and CCPA. This book is an amalgamation of Shane's in-depth knowledge in data science applications and malware defense techniques, designed to serve as a practical manual for bolstering day-to-day cyber resilience.

Summary:

Unlock the secrets of malware data science with cutting-edge techniques, AI-driven analysis, and international compliance standards to stay ahead of the ever-evolving cyber threat landscape Key Features Get introduced to three primary AI tactics used in malware and detection Leverage data science tools to combat critical cyber threats Understand regulatory requirements for using AI in cyber threat management Purchase of the print or Kindle book includes a free PDF eBook Book Description 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. What you will learn Understand the science behind malware data and its management lifecycle Explore anomaly detection with signature and heuristics-based methods Analyze data to uncover relationships between data points and create a network graph Discover methods for reverse engineering and analyzing malware Use ML, advanced analytics, and data mining in malware data analysis and detection Explore practical insights and the future state of AI's use for malware data science Understand how NLP AI employs algorithms to analyze text for malware detection Who this book is for This book is for cybersecurity experts keen on adopting data-driven defense methods. Data scientists will learn how to apply their skill set to address critical security issues, and compliance officers navigating global regulations like GDPR and CCPA will gain indispensable insights. Academic researchers exploring the intersection of data science and cybersecurity, IT decision-makers overseeing organizational strategy, and tech enthusiasts eager to understand modern cybersecurity will also find plenty of useful information in this guide. A basic understanding of cybersecurity and information technology is a prerequisite.

Contents:

Cover

Title Page

Copyright and Credits

Dedication

Foreword

What the experts say

Contributors

Table of Contents

Preface

Part 1- Introduction

Chapter 1: Malware Science Life Cycle Overview

Combining malware

Worms and Trojans combination

Ransomware and spyware combination

Macro malware and ransomware

Managing malware

Collection

Analysis

Detection

Prevention

Mitigation

Reporting

Summary

Chapter 2: An Overview of the International History of Cyber Malware Impacts

The evolution of cyber threats and malware

Impacts on international relations and security

Impacts on the economy and cybercrime

The future of malware

Expanded viewpoint on the impacts on international relations and security

Expansion on cybercrime impacts on the general economy

Direct financial impacts of malware - a global overview

Ransomware's global economic impact - a continental overview

Ransomware's economic impact in North America - a deeper look

Ransomware's economic impact in Asia - a detailed examination

Ransomware's economic impact in Africa - an in-depth analysis

Ransomware's economic impact in South America - an extensive exploration

Economic impacts versus socio-economic impacts

Ransomware attacks and their impact on employment - an in-depth perspective

Ransomware attacks and their impact on public services - an elaborate examination

Ransomware and inequality - a closer look at the impact on small businesses

Policy, regulations, and their downstream impact on smaller businesses and public services

Regulatory changes due to malware impacts on small and mid-scale businesses

A deeper dive into the operational challenges

Translating operational challenges into increased cost

Expansion of economic and socio-economic impacts on key industries globally.

Key downstream impacts on key industries globally

The use of AI systems with malware

Cybersecurity, malware, and the socio-economic fabric

Part 2 - The Current State of Key Malware Science AI Technologies

Chapter 3: Topological Data Analysis for Malware Detection and Analysis

The mathematics of space and continuous transformations

A deeper dive into the "shape of the data"

How TDA creates a multi-dimensional data representation

Transforming a malware binary into a topological space

Homology

Persistence homology distinguishes meaningful patterns from random data fluctuations

Improving detection algorithms to predict the behavior of new malware

TDA - comparing and contrasting the persistence diagrams of different software

Using malware persistence diagrams to classify unknown software

Persistence homology - filtering noise to find meaningful patterns

Classifying unknown malware with characteristic persistent features

Leveraging classification to manage threat response

A deeper dive - employing TDA for threat management

Chapter 4: Artificial Intelligence for Malware Data Analysis and Detection

AI techniques used in malware data analysis

Supervised learning

Challenges and considerations

Unsupervised learning

Deep learning for malware analysis deep learning

Benefits of AI techniques in malware data analysis

Challenges in AI-based malware analysis

Benefits of AI in malware detection

Enhanced detection accuracy

Future prospects

Improved adversarial defense

Hybrid approaches

Explainable AI (XAI)

Chapter 5: Behavior-Based Malware Data Analysis and Detection

Behavior-based malware data analysis

Data collection

Behavior analysis

Behavior-based malware detection.

The concept of proactive behavior-based malware detection

The concept of malware's behavioral characteristic

Operational aspects of software behavior data collection

Operational aspects of behavior modeling using machine learning or AI

Operational aspects of behavior monitoring

Operational aspects of malware behavior-based response

Operational aspects of anomaly detection

Operational aspects of specification-based techniques

Normalcy and anomaly detection

Concept of normalcy

Concept of anomaly detection

Future concepts of normalcy and anomaly detection

Overcoming the increased complexity of evolving cyber threats

Handling increased complexity and data volume

Navigating privacy regulations

Mitigating evolving cyber threats

Implementing the solutions

Starting with the basics - organizational capability maturity

The relationship between the CMMI maturity process and the increased complexity of threat management

Operational challenges and mitigation strategies to enhance organizational cybersecurity capabilities

Part 3 - The Future State of AI's Use for Malware Science

Chapter 6: The Future State of Malware Data Analysis and Detection

The future state of advanced ML and AI integration in malware detection

Beyond signature-based detection

The future state of automated malware analysis

Why manual processes are no longer viable

The dawn of automated malware analysis

The future state of cloud-based TI

The current landscape of TIPs

The advent of cloud-driven TI

The future state of integration of big data analytics in cybersecurity

Understanding the magnitude of modern data

The imperative for big data analytics

Integration of AI - the game-changer.

The future state of deeper OS-level integrations in malware detection

The current state of malware detection

The rationale behind OS-level integrations

Potential avenues for deeper OS-level integrations

Benefits of deeper OS-level integrations

The future state of post-quantum cryptography in countering quantum-vulnerable malware

Understanding the quantum threat

Post-quantum cryptography - the new frontier

Integration in malware detection and defense

Challenges ahead

The future state

The future state of proactive defense mechanisms in cybersecurity

Why proactive defense?

The cornerstones of proactive defense

Advantages of a proactive stance

Challenges in implementation

The road ahead - a dynamic defense ecosystem

The future state of enhanced sandbox environments in cybersecurity

Modern challenges - evolving malware tactics

The vision - next-generation sandboxes

Chapter 7: The Future State of Key International Compliance Requirements

The future state of global data privacy regulations

The future state of AI ethics and governance standards

The future state of cybersecurity and risk management

The future state of supply chain transparency

The future state of financial crime prevention

The future state of cross-border data flow regulations

The future state of climate change regulations

The future state of blockchain and digital identity

The future state of RegTech

The future state of geopolitical dynamics

Chapter 8: Epilogue - A Harmonious Overture to the Future of Malware Science and Cybersecurity

Appendix

Index

About Packt

Other Books You May Enjoy.

Notes:

Description based on print version record.

ISBN:

9781804615706

1804615706

OCLC:

1411278491