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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

The future state of financial crime prevention

Compliance requirements related to anti-money laundering (AML), counter-terrorism financing (CTF), and other financial crimes are likely to evolve. Regulators might adopt more sophisticated approaches, leveraging AI and machine learning to detect and prevent illicit financial activities.

In this section, we will delve deeper into the practical implications and strategic considerations surrounding the integration of AI and machine learning in combating financial crimes. Exploring a series of actionable steps and insights, we aim to equip organizations in the financial industry with the knowledge and strategies needed to thrive in this ever-evolving landscape of compliance.

The financial sector has long been a target for criminal activities, necessitating robust compliance measures to combat money laundering, fraud, and other financial crimes. Criminal organizations often exploit the complexity of financial systems to launder money...

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