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.
Perfect for: Network engineers, SOC analysts, cybersecurity students, and developers with basic networking knowledge (OSI model, TCP/IP basics) who want to add AI-driven traffic analysis to their skill set.
See Every Byte. Understand Every Threat.
Most network engineers and security professionals can ping a host or read a firewall log — but very few can crack open a packet capture and truly understand what the network is saying. This course changes that. You'll go from the fundamentals of how data actually travels across a network to writing AI-assisted scripts that automatically flag anomalies, classify traffic, and surface threats hiding in plain sight.
Hands-On From Day One
Every lesson is built around real packet captures, real tools (Wireshark, Zeek, Scapy, and Python), and real-world scenarios pulled from production environments and public threat datasets. You won't be watching slides — you'll be capturing live traffic, engineering features from raw PCAPs, and training lightweight ML models to do the heavy lifting for you.
What Makes This Course Different
The networking world is flooded with "intro to Wireshark" tutorials, but almost nothing bridges the gap between classical packet analysis and modern AI/ML workflows. This course is that bridge. You'll learn how protocol dissection feeds into feature engineering, how unsupervised learning can detect beaconing malware, and how to build a lightweight anomaly detection pipeline you can actually deploy.
Who Should Take This Course?
Whether you're a network engineer wanting to level up your security skills, a SOC analyst drowning in alerts, or a developer curious about what's really happening on the wire — this course gives you a concrete, portfolio-worthy skill set that employers and clients are actively looking for right now.
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
Curriculum
6 modules · 18 lessons
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.
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