Networking & AI Packet Analysis
Master the art of capturing, dissecting, and intelligently analyzing network traffic using modern tools and AI-powered techniques — from raw packets to actionable security insights.
What you'll learn
What you'll be able to do
- Capture and filter live and historical network traffic using Wireshark, tcpdump, and Scapy
- Dissect and interpret packets at every layer of the TCP/IP stack — Ethernet, IP, TCP/UDP, and application protocols
- Extract structured features from raw PCAP files for use in machine learning pipelines
- Build and evaluate supervised ML classifiers to detect malicious vs. benign traffic
- Implement unsupervised anomaly detection to surface zero-day-style threats without labeled data
- Use Zeek (formerly Bro) to generate rich network logs and integrate them with Python-based AI workflows
- Construct an end-to-end automated packet analysis pipeline deployable in a home lab or cloud VM
- Interpret and communicate findings from AI-flagged network events for incident response scenarios
How it works
A school that adapts to you
This isn't a set of static videos. Every lesson is generated live and tuned to where you actually are.
We learn your level
A quick placement check tailors your starting point so you're never bored or lost.
Lessons adapt as you go
Each lesson is written for your pace and your goal, adjusting as your skills grow.
Your AI coach keeps you moving
Checkpoints, feedback, and gentle nudges turn progress into a real result.
The curriculum
What's inside your school
6 modules · 18 lessons
Networking Foundations for Analysts
Establish a rock-solid understanding of how data moves across networks — from physical frames to application payloads — so you can reason about packets at any layer with confidence.
- 1.1The TCP/IP Stack RevisitedIncluded
- 1.2DNS, HTTP, and TLS: The Protocols You'll See MostIncluded
- 1.3Reading and Writing PCAP FilesComing soon
Deep-Dive Packet Analysis with Wireshark & Scapy
Move beyond clicking around Wireshark and into programmatic, repeatable packet analysis using Scapy and advanced Wireshark features.
- 2.1Advanced Wireshark: Filters, Coloring Rules & StatisticsComing soon
- 2.2Scapy Fundamentals: Crafting and Parsing Packets in PythonComing soon
- 2.3Zeek for High-Fidelity Network LogsComing soon
Feature Engineering from Raw Traffic
Transform raw packets and Zeek logs into clean, structured feature sets that machine learning models can actually learn from — the most critical and most overlooked step in the AI pipeline.
- 3.1Thinking in Flows: Aggregating Packets into ConversationsComing soon
- 3.2Extracting Meaningful FeaturesComing soon
- 3.3Handling Real-World Data Quality IssuesComing soon
Supervised Learning for Traffic Classification
Train, evaluate, and tune classification models that can distinguish benign traffic from known attack categories using labeled network datasets.
- 4.1Choosing and Training a ClassifierComing soon
- 4.2Evaluation Beyond Accuracy: ROC, PR Curves & ThresholdsComing soon
- 4.3Keeping Models Fresh: Concept Drift and RetrainingComing soon
Unsupervised Anomaly Detection
When you don't have labels — which is most of the time — unsupervised methods let the data surface its own outliers. Learn Isolation Forest, Autoencoders, and clustering-based approaches on real traffic.
- 5.1Isolation Forest for Network AnomaliesComing soon
- 5.2Autoencoder-Based Anomaly DetectionComing soon
- 5.3Clustering Traffic for Behavioral ProfilingComing soon
Building & Deploying Your Analysis Pipeline
Tie everything together into a documented, automated pipeline that ingests PCAPs, extracts features, runs AI inference, and produces human-readable alerts — ready for your portfolio and your network.
- 6.1Designing the End-to-End PipelineComing soon
- 6.2Alert Triage and ExplainabilityComing soon
- 6.3Portfolio Packaging and Next StepsComing soon
Questions
Frequently asked
Your teacher
A note from your teacher
Fernando Segui
Hi, I'm excited to share this course with you — it sits at the intersection of two fields I've spent years working in: network engineering and applied machine learning. I've spent time on the wire troubleshooting production outages, hunting threats in SOC environments, and building data pipelines that turn raw telemetry into actionable intelligence. What I kept finding was a massive gap: engineers knew protocols cold but had never touched a scikit-learn estimator, and data scientists could build beautiful models but had no idea what a SYN flood looked like on the wire. This course is my attempt to close that gap permanently. Every lab, every dataset, and every exercise in here is something I've personally used in real work. I'm glad you're here — let's dig in.
— Fernando Segui
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- 6 modules, 18 lessons
- AI-adaptive lessons tuned to your level
- Quizzes & checkpoints to lock in progress
- Your own AI learning coach
- Learn on any device, at your pace
- Full access for as long as you're subscribed
16 more lessons coming soon at no extra cost.