AI for Cyber Defense
Detect Phishing and Network Threats with Machine LearningBy Lena Fontaine
Length8h 2m
About this audiobook
Machine learning for cybersecurity, AI threat detection, and automated defense are the most sought-after skills in the modern security landscape. This practical, hands-on guide by Lena Fontaine takes you from beginner to pro, covering anomaly detection, phishing, malware classification, and building production-ready security models. You'll learn to detect threats and automate defense with machine learning, using real datasets and Python code. [placeholder] and [placeholder] are among the few authors who have tackled this topic, but this book goes deeper into practical implementation. From setting up your environment to deploying models at scale, each chapter builds on the last. You'll start with core ML concepts and cybersecurity fundamentals, then dive into supervised and unsupervised learning for intrusion detection, email phishing filters, and malware family classification. Advanced chapters cover ensemble methods, deep learning for network traffic analysis, and adversarial AI. By the end, you'll have a complete toolkit to build and evaluate your own defense systems. Whether you're a security analyst, data scientist, or developer, this book empowers you to stay ahead of evolving threats.
What You'll Learn
Implement anomaly detection for network traffic and user behavior
Build phishing classifiers using natural language processing and feature engineering
Classify malware with decision trees, random forests, and neural networks
Automate incident response with reinforcement learning and rule engines
Deploy models with Docker, Flask, and cloud services
Evaluate model performance with precision, recall, and ROC curves
With over 200 pages of code examples, case studies, and exercises, this is the definitive resource for applying AI to cyber defense. Start your journey today and transform how you protect digital assets.
Audiobook details
GenreTechnology
Length8 hrs 2 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
LanguageEnglish
Table of contents
1Introduction
12Chapter 10 — Automating Incident Response with Reinforcement Learning
2Preface
13Chapter 11 — Endpoint Detection and Response with ML
3Chapter 1 — Setting Up Your AI Security Lab
14Chapter 12 — Adversarial Machine Learning Defense
4Chapter 2 — Machine Learning Fundamentals for Security
15Chapter 13 — Deploying Models to Production
5Chapter 3 — Data Ingestion and Feature Engineering for Security Logs
16Chapter 14 — Monitoring and Retraining in Production
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6Chapter 4 — Phishing and Malicious URL Detection
17Chapter 15 — Scaling for Large Environments
7Chapter 5 — Network Intrusion Detection with Machine Learning
18Chapter 16 — Cloud Security with AI
8Chapter 6 — Malware Classification from Static and Dynamic Analysis
19Chapter 17 — Container and Kubernetes Security with ML
9Chapter 7 — Anomaly Detection for Insider Threats and Zero-Days
20Chapter 18 — The SOC of the Future: Ethics, Careers, and Beyond
10Chapter 8 — Threat Intelligence Feeds and Enrichment
21About the Author
11Chapter 9 — Threat Hunting with Large Language Models
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