SAVE THE DATE: June 7th, 2023 1 PM ET


LIVE TRAINING: Introduction to PYTHON for Programming 

October 12th @12 PM EST

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Price: $147

Regular price $210, discounted 10%
  • 4 hour immersive session

  • Hands-on training with Q&A

  • Recording available on-demand

  • Certification of Completion

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Pricing: $99

Price: $75 with Annual Ai+ Premium Subscription

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  • Access to all on-demand Ai+ Sessions (more than 100)
  • Previous ODSC Conference recordings
  • $200 credit for upcoming ODSC Conferences
  • Machine Learning Certification
  • Deep Learning Bootcamp

Price: $99 (price already includes 30% discount)

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  • Hands-on training with QA
  • Certification of Completion

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Meet Your Instructor

Howard Poston

Howard Poston is a cybersecurity researcher with a background in blockchain, cryptography and malware analysis. He has a master’s degree in Cyber Operations from the Air Force Institute of Technology and two years of experience in cybersecurity research and development at Sandia National Labs. He currently works as a freelance consultant providing training and content creation for cyber and blockchain security.

Course Overview

What’s the plan? 

Network traffic data can be a rich source of information for cybersecurity and IT management. Malware is delivered and performs command and control communications over the network, and software sends and receives a wealth of data over the network.
The course provides an introduction to analyzing network traffic data with Python. Python is an ideal tool for this task because it is freely available and has numerous modules that support network traffic analysis, data science, and other tasks.
This course will build familiarity and skills in network traffic analysis via a series of hands-on exercises and examples. By examining, modifying, and building custom scripts for network traffic analysis, you’ll how to access and select network traffic data, analyze it, and interpret the results.

Learning Objectives

  • Understand how to analyze network traffic, including what features to extract and how to analyze them

  • Use Python and Scapy to analyze network traffic in packet capture files and live captures

  • Develop custom Python scripts to answer questions with network traffic data

Why Enroll?

  • Learn about LLMs, one of the hottest areas in AI today

  • Learn how ChatGPT was built / what makes it possible

  • Learn which use cases LLMs could solve

  • Learn how LLMs are being used across finance, healthcare, legal, and education

  • Learn practical advice on using LLMs in production

Learning Objectives

  • What are the benefits of LLM?

  • What are the challenges?

  • What tasks can large language models perform?

  • Applying LLMs to use cases and apps across different industries (including the future possibilities)

  • Case study in finance

  • Case study in healthcare

  • Case study in legal

  • Case study in education

Course Outline

What’s the plan? 

Matt Harrison has been working with Python and data since 2000. He has a computer science degree from Stanford. He has worked at many amazing companies, created cool products, wrote a couple books, and taught thousands Python and Data Science. Currently, he is working as a corporate trainer, author, and consultant through his company Metasnake, which provides consulting and teaches corporations how to be effective with Python and data science.

Course Overview

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Course Outline

Module 1:
Getting Started with Network Traffic Analysis in Python

  •  Setting up the development environment (libraries, packet capture files, etc.)
  •  Looking at a packet capture in Wireshark (provides better visualizations)
  • Getting started with Scapy
  • Loading a packet capture into Scapy
  • Viewing capture contents
  • Accessing fields of a traffic capture

Module 2:
Feature Selection for Network Traffic Analysis

  •  Explore the structure of a network packet in Wireshark/Scapy
  • Identify the fields that would be useful/useless for network traffic analysis (for example, server ports are useful, while client ports are not since they are random)
  • Write code that extracts features of interest for further analysis
  •  Perform basic analysis of traffic (i.e. clustering, etc.) using extracted features

Module 3:
Level Traffic Analysis

  • Discuss the concept of network flows (i.e. high-level header data with no packet contents)
  • Write code to convert a packet capture or live traffic capture to flow data
  • Generate a network map with flow data
  • Classify systems based on role in the organization (end-user systems, various types of servers, etc.)
  • Identify potential data exfiltration with flow data
  • Identify anomalous sessions for future analysis (i.e. differentiating a successful login attempt from a failed one, etc.)

Module 4:
Packet-Level Traffic Analysis

  • Discuss the pros and cons of packet-level analysis
  • Write code to extract packet payloads, HTTP headers, and other features of interest
  • Extract credentials and other sensitive data from unencrypted communications
  • Identify encoded and encrypted data within packet contents
  • Extract potential indicators of compromise (IoCs) to identify malicious traffic
  • Carve files from network traffic for further analysis

🗝️Key Details





June 7th, 2023

1PM ET, 10 AM PT




  • Knowledge of Python, Network Traffic Analysis, and Data Science is helpful but not required
  • Python (and various Python libraries), Wireshark

Upcoming Live Training




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