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Udemy - Machine Learning for Cybersecurity - AI Threat Detection

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Udemy - Machine Learning for Cybersecurity - AI Threat Detection (Size: 1.5 GB)
  2 - Set Up the Development Environment
  2 - Environment Setup Native Linux & Windows WSL2.mp4 42.9 MB
  3 - Machine Learning Foundations for Security
  4 - Cybersecurity Detection Concepts
  10 - Alert Triage & Incident Attribution Workflows.mp4 19.9 MB
  5 - Design and Generate the Security Dataset
  11 - Designing the Synthetic Security Telemetry Schema.mp4 12.3 MB
  12 - Simulating Benign Background Workstation Behavior.mp4 13.6 MB
  13 - Injecting Realistic Brute-Force Authentication Spikes.mp4 10.5 MB
  14 - Modeling Covert Data Exfiltration & Network Surges.mp4 11.7 MB
  15 - Reproducible Random Seed Management & CSV Generation.mp4 9.7 MB
  16 - Data Quality Validation & Label Integrity Auditing.mp4 10.8 MB
  6 - Data Preparation and Leakage Prevention
  1 - Security Telemetry & Zero-Leakage Data Preparation Quiz.html 18.5 KB
  17 - Telemetry Inspection, Feature Decoupling, and Stratified Partitioning.mp4 29.9 MB
  18 - Zero-Leakage Feature Scaling and Pipeline Serialization.mp4 23 MB
  7 - Isolation Forest Anomaly Detection Fundamentals
  19 - How Isolation Trees Partition Feature Space.mp4 39.2 MB
  20 - Contamination Tuning, Anomaly Scoring & Wave Crest Mathematics.mp4 47.1 MB
  8 - Train, Evaluate and Save Isolation Forest
  21 - Training the Isolation Forest on Normal-Only Baselines.mp4 54.9 MB
  22 - Evaluation Against Unseen Anomalies, Threshold Tuning & Pickle Export.mp4 59.8 MB
  9 - Random Forest Fundamentals
  23 - Decision Trees, Bagging & Ensemble Decorrelation.mp4 38 MB
  24 - Multi-Tree Voting & Supervised Attack Classification.mp4 44.4 MB
  6 - Signatures vs Behavioral Anomalies vs Machine Learning.mp4 19.8 MB
  7 - The Anatomy of an Enterprise Security Event.mp4 20.6 MB
  8 - Threat Categories Brute Force, Exfiltration & Miners.mp4 20.3 MB
  9 - Base Rate Fallacy & False Positive Economics in the SOC.mp4 23.6 MB
  4 - What Machine Learning Is (and Is Not) in Cybersecurity.mp4 44.5 MB
  5 - Events, Features, Labels & Security-Specific Evaluation Metrics.mp4 37.2 MB
  3 - Virtual Environments, Dependency Pinning & Smoke Testing.mp4 49.6 MB
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  test_alert.py 1.5 KB
  test_api.py 1.3 KB
  test_dashboard.py 716.8 B
  test_detector.py 4.2 KB
  test_evaluate.py 2 KB
  test_golden_events.py 5 KB
  test_isolation_forest_model.py 1.4 KB
  test_preprocessing.py 2.5 KB
  test_random_forest_model.py 1.8 KB
  test_risk_engine.py 1.2 KB
  test_simulate.py 2.3 KB
  test_train.py 2 KB
  ~Get Your Files Here !
  1 - Welcome and Project Preview
  1 - Project Architecture & End-to-End Walkthrough.mp4 42.4 MB
  10 - Train, Evaluate and Save Random Forest
  11 - Compare the Models and Build the Prediction Pipeline
  12 - Design the Combined Risk Engine
  13 - Expose Threat Detection Through FastAPI
  14 - Security Monitoring Dashboard
  15 - Near-Real-Time Event Simulation & 41-Test Master Security Suite
  16 - Performance, Model Security, and Responsible Use
  17 - Final Capstone and System Demonstration
  18 - Code
  40 - Code (Description).html 614.4 B
  40 - Code.html 5.3 KB
  ml-security
  README.txt 6.9 KB
  RUN-COMMANDS.txt 10.2 KB
  api
  dashboard
  app.py 2.8 KB
  data
  raw
  KDDTest+.txt 3.3 MB
  KDDTrain+.txt 18.2 MB
  gitignore 307.2 B
  models
  isolation_forest.pkl 4.2 MB
  keys
  aes256.key 0 B
  ed25519_signing.key 0 B
  ed25519_verify.key 0 B
  notebooks
  class_distribution.png 16.3 KB
  data_analysis.ipynb 45 KB
  isolation_forest_exploration.ipynb 64.2 KB
  random_forest_exploration.ipynb 100 KB
  requirements.txt 204.8 B
  src
  alert.py 1.6 KB
  config.py 2.4 KB
  detector.py 4.5 KB
  evaluate.py 3.6 KB
  generate_dataset.py 4.6 KB
  isolation_forest_model.py 3 KB
  model_integrity.py 8.4 KB
  preprocessing.py 3.5 KB
  random_forest_model.py 2.3 KB
  risk_engine.py 2.3 KB
  simulate.py 4.6 KB
  tests
  golden
  attack
  brute_force_login.json 204.8 B
  crypto_miner.json 204.8 B
  data_exfiltration.json 204.8 B
  edge_case
  failed_logins_just_under_threshold.json 204.8 B
  normal
  normal_light_activity.json 204.8 B
  normal_moderate_activity.json 204.8 B
  simultaneous_cpu_and_login_spike.json 204.8 B
  mass_file_modification.json 204.8 B
  suspicious_process_activity.json 204.8 B
  train.py 2.3 KB
  preprocessor.pkl 1.2 KB
  random_forest.pkl 1.8 MB
  security_events.csv 411.1 KB
  main.py 2.5 KB
  39 - Full System Reproduction, Git Discipline, Portfolio Packaging & Course Wrap-Up.mp4 36.5 MB
  37 - End-to-End Performance Benchmarking, Costs, and Model Drift.mp4 42.1 MB
  38 - Model Integrity, Cryptographic Defense, and Threat Modeling.mp4 73.9 MB
  35 - Near-Real-Time Event Simulation & Live Telemetry Streaming.mp4 54.2 MB
  36 - The 41-Test Master Security Suite & Golden Attack Verification.mp4 57.8 MB
  33 - Streamlit Reactive Loop, Caching Architecture & Layout Geometry.mp4 61.4 MB
  34 - Real-Time Telemetry Visualization, Triage Filters & AppTest Verification.mp4 56.2 MB
  31 - REST API Architecture Pydantic Validation, Schemas & Model Caching.mp4 73.3 MB
  32 - Live Production Execution cURL Scoring, Automated Tests & Error Handling.mp4 79.1 MB
  2 - Dual-Model ML & The 3x3 Risk Engine.html 18.2 KB
  29 - Translating Raw Probability Floats into Actionable SOC Tiers.mp4 41.1 MB
  30 - The 4-Tier Decision Matrix & Automated Reason Generation.mp4 48.6 MB
  27 - Model Comparison When Isolation Forest Fails vs When Random Forest Fails.mp4 41.8 MB
  28 - The Unified ThreatDetector Class Vectorized Dual-Model Inference.mp4 34.2 MB
  25 - Training Random Forest on Labeled Security Incidents.mp4 42.6 MB
  26 - Precision-Recall Tradeoffs, Confusion Matrix & Model Persistence.mp4 30.8 MB
  ▲ 97 total files

Description


Machine Learning for Cybersecurity: AI Threat Detection
https://WebToolTip.com
Published 10/2026

MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

Language: English | Duration: 3h 44m | Size: 1.47 GB
Build dual ML models (Isolation Forest & Random Forest), a SOC risk engine, FastAPI REST API, and Streamlit dashboard
What you'll learn

Machine Learning for Cybersecurity: Build AI Threat Detection

Build an end-to-end, production-grade defensive cybersecurity threat detection system in Python from scratch

Train an Isolation Forest exclusively on benign network telemetry to detect novel zero-day attacks without contamination leakage

Train and evaluate a Random Forest classifier on labeled attack patterns (brute force, crypto mining, data exfiltration)

Build an interactive Security Operations Center (SOC) monitoring dashboard in Streamlit featuring real-time KPI metrics and severity triage filters

Verify pipeline integrity with a rigorous 41-test master pytest suite running against real trained models and golden attack fixtures
Requirements

Basic Python Familiarity

Zero Prior Machine Learning or Cybersecurity Knowledge Required

A Standard Computer (Windows, macOS, or Linux)

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