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Cybersecurity

AI-Enhanced Network & Packet Analysis

Group Training

Overview

This course teaches students the fundamental concepts, methodologies, and tools necessary to analyze network traffic for the purposes of intrusion and threat detection, network defense, and low-profile incident investigation — now enhanced with AI-driven insights. While performing incident response or investigations, students will learn to determine where malicious activity is occurring and take their analytical skills to the next level.

The hands-on course begins with the role of network packet analysis in Computer Network Operations (CNO). Students learn core TCP/IP and Ethernet concepts and perform packet capture and analysis of self-generated network traffic, using tcpdump, tshark, and Wireshark, alongside emerging AI-assisted methods that accelerate anomaly detection, protocol identification, and data correlation. These include the use of large language models (LLMs) for summarization and triage, lightweight ML models for anomaly detection, and AI-assisted rule/signature generation to enhance detection engineering and streamline analysis workflows.

The course emphasizes both human expertise and AI augmentation, giving students practical experience in automated log analysis, packet classification models, and the integration of AI-based assistants into packet forensics operations. By combining foundational networking knowledge with AI-driven analytics, students develop the ability to interpret complex network behaviors with speed, accuracy, and critical reasoning.

Throughout the course, students will examine real packet captures illustrating exploits, reconnaissance techniques, and advanced network attacks. Students will practice traditional command-line filtering and GUI analysis, then apply AI tools to automatically extract indicators, summarize multi-session attacks, prioritize alerts, and assist in crafting BPF/Wireshark filters and IDS rules.

The course culminates in a capstone exercise in which students combine human analysis with AI-assisted workflows to detect, explain, and document complex network threats and breaches.

Duration

4-5 days

Course Modules

  • TCP/IP Review
  • The Protocols
  • Basic tcpdump
  • Advanced tcpdump
  • Wireshark
  • Practical Exercise

Who Should Take This Course

Prerequisites

  • CompTIA Network+ certification or equivalent knowledge
  • Working knowledge of TCP/IP fundamentals or equivalent experience (one year recommended)
  • CCNA is recommended but not required
  • Experience with basic Linux command line functions
  • Working knowledge of information assurance and network security principles.

Course Outline

AI-Enhanced Network & Packet Analysis

Module 00: TCP/IP Review

  • OSI vs Internet Model
  • Physical and Logical Addresses
  • Services and Ports
  • Domain Name System
  • Routing& Traffic Types
  • IP Protocols: TCP/UDP
  • Media Access Control
  • Network Communications

AI Infusion:

  • AI tools and ethics
    • LLM behavior and limitations in cybersecurity contexts.
    • Data privacy and model risk when using cloud-based AI tools for analysis.
    • Ethical implications of AI use in packet inspection and monitoring
  • AI-assisted Visualization of Network Topology
  • AI-based anomaly detection

Module 01: The Protocols

  • Link Layer
    • Ethernet
    • Address Resolution Protocol
  • Network Layer
    • Internet Protocol
    • Internet Control Message Protocol
  • Transport Layer
    • Transmission Control Protocol
    • User Datagram Protocol
  • Application Layer
  • Dynamic Host Configuration Protocol
  • Domain Name System
  • Hypertext Transfer Protocol

AI Infusion:

  • AI classification models
  • Automated protocol documentation summaries employing LLMs
  • AI-aided correlation

Module 02: Basic Tcpdump

  • Sniffing Basics
  • Capture and read files
  • Command line options
  • Filters: hosts, ports and protocols
  • Decrypting output

AI Infusion:

  • Generating BPF filter expressions using AI assistants
  • Summarizing packet captures using LLMs
  • AI-assisted flag parsing

Module 03: Advanced Tcpdump

  • Advanced expressions and primitives
  • Qualifiers
  • Expression combinations
  • Offsets and specific byte identification
  • Byte range filters
  • Bit masking

AI Infusion:

  • Applying AI tools to suggest optimized capture filters
  • Detection of hidden command-and-control patterns using ML models
  • Leveraging AI code-generation

Module 04: Wireshark

  • Creating customized capture filters
  • Display filters
  • Filters and target lists
  • Session reconstruction
  • Dangers of WiFi

AI Infusion:

  • Summarizing multi-session captures using LLMs
  • AI-based visualization
  • AI-driven decryption assistance
  • AI detection vs human inspection

Module 05: Practical Exercise

  • Analyze packet captures from a victim network.
  • Identify and document network attacks and anomalies.
  • Present findings in a structured report.

AI Infusion:

  • Combining human packet analysis with AI triage
  • Extracting IOCs using AI tools
  • Leveraging AI for report drafting
  • Evaluation of AI reliability and bias
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