2026年新课，3小时44分实战视频，教你用Python构建端到端AI威胁检测系统：孤立森林识别零日漏洞、随机森林分类已知攻击，结合FastAPI与Streamlit打造23毫秒响应的SOC看板，实现100%检出与0%误报。
原始标题：Machine Learning for Cybersecurity: AI Threat Detection
该课程是一门2026年10月新出的纯本地CPU运行、3小时44分钟的全英文实战视频课，旨在教你用 Python 从零构建企业级机器学习网络威胁检测系统：通过结合孤立森林（识别未知零日漏洞）和随机森林（分类暴力破解/挖矿等已知攻击）双模型打造复合风险引擎，并利用 FastAPI 部署每条响应仅23毫秒的异步API、用 Streamlit 搭建实时 SOC 安全看板，最后通过 SHA-256防篡改和 41个 pytest 自动化测试确保系统达到100%检出和0%误报，非常适合想从静态规则升级为 AI 行为防御的安全、算法及 Python 开发人员。
Published 10/2026
MP4 | Video: h264, 1920×1080 | 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)
Description
This course contains the use of artificial intelligence.
Traditional static firewall rules break the moment an attacker changes a single port or byte, flooding Security Operations Center (SOC) teams with thousands of false alarms. In this hands-on course, you will build, test, and deploy a complete, end-to-end Machine Learning Threat Detection and Intrusion Detection System from scratch in Python.
You will engineer a production-grade security pipeline using Python, scikit-learn, FastAPI, Pydantic, Streamlit, and pytest that runs 100% locally on any standard computer
– Zero-Leakage Telemetry Preprocessing: Generate reproducible enterprise host and network logs and scale 6 core security features (CPU, Memory, Network Bytes, Logins, File Writes, and Connections) using a StandardScaler fit strictly on training splits.
– Unsupervised Zero-Day Anomaly Detection: Train a 200-tree Isolation Forest exclusively on clean baseline traffic to isolate novel, unseen zero-day threats without contamination leakage.
– Supervised Threat Classification: Train a 300-tree Random Forest classifier on labeled attack patterns, including brute-force login floods, stealthy crypto-miners, covert data exfiltration, and mass file modification.
– The 3×3 Combined Risk Engine: Synthesize continuous anomaly scores and attack probabilities through a deterministic lookup matrix that assigns LOW, MEDIUM, HIGH, or CRITICAL severity tiers alongside plain-English explanations for SOC analysts.
– Asynchronous FastAPI Service & Live Streamlit SOC Dashboard: Expose real-time predictions via /predict and /health REST API endpoints, visualize live KPIs and severity distributions in Streamlit, and stream telemetry at 23 milliseconds per event.
– Cryptographic Model Security & 41-Test Master Suite: Protect serialized model files against tampering with SHA-256 hash verification and validate the entire pipeline with a 41-test pytest suite achieving 100% attack detection and 0% false alarms on golden attack benchmarks.
How This Course Is Taught
Every concept is illustrated with Pure Diagrammatic Animations—active hardware diagrams, decision trees, coordinate hyperplanes, and score tachometers with zero walls of bullet-point slides—followed by line-by-line full-screen Python code walkthroughs and live terminal execution.
Who this course is for
SOC Analysts & Cybersecurity Professionals** who want to understand how modern AI threat detection engines work under the hood and upgrade from static regex signatures to behavioral machine learning
Machine Learning Practitioners & Data Scientists** seeking real-world, practical applications of anomaly detection and classification on security telemetry, APIs, and real-time streaming pipelines
Python Developers & Software Engineers** looking to break into the high-demand cybersecurity and AI defense space by building an end-to-end security project
DevSecOps, Cloud Engineers & Security Operations Teams** aiming to automate threat triage, build transparent risk scoring engines, and eradicate alert fatigue
此处内容需要权限查看
会员免费查看
