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Threat Hunting Professional Training In Brisbane
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In Infosectrain, Grab the Threat Hunting Training to achieve a deep understanding of Threat Hunting techniques and the role of Threat Hunters. Our training is curated with the in-depth concepts of Threat Hunting methods and helps you to get certified for the Cyber Threat Hunting Professional exam.

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Course Highlights

  • 40 hours Instructor-led Training
  • Learn from Industry Experts
  • Highly Interactive and Dynamic Sessions
  • 20+ AI-driven Hands-On Labs
  • Learn with Real-World Scenarios
  • Capstone Challenges
  • Career Guidance and Mentorship
  • Extended Post Training Support
  • Access to Recorded Sessions

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Course Description

The AI-powered Advanced Threat Hunting, Digital Forensics, and Incident Response (DFIR) Training equips security professionals with elite skills to detect, investigate, and respond to sophisticated cyber threats across enterprise environments. Participants learn core DFIR competencies, including persistence, lateral movement, and credential abuse hunting, memory and disk forensics, malware analysis, and network-based C2 detection.
 
What sets this course apart is the integration of AI-accelerated workflows, enabling analysts to handle massive datasets, automate repetitive tasks, and generate actionable reports faster, while retaining full control over investigative judgment, hypothesis testing, and evidence interpretation. By combining hands-on labs, real-world scenarios, and portable AI techniques, learners gain practical experience that applies to any enterprise environment, preparing them to deliver precise, executive-ready investigations efficiently and confidently.

Target Audience

This training is ideal for:

  • SOC Analysts (Tier 2+) seeking to advance beyond alert triage to proactive hunting
  • Incident Responders looking to enhance investigation techniques and efficiency
  • Security Engineers responsible for building detection engineering capabilities
  • Digital Forensic Analysts expanding into threat hunting methodologies
  • Penetration Testers who want to understand defensive detection techniques
  • Security Architects responsible for designing security monitoring solutions

Pre-Requisite

Required Technical Knowledge

  • Windows Systems (Essential)
    • Windows Event Log analysis (Security, System, Application logs)
    • Registry structure and common keys related to security
    • Windows authentication mechanisms and security tokens
    • PowerShell fundamentals and security-related cmdlets
    • Windows services, scheduled tasks, and startup mechanisms
  • Networking Fundamentals (Essential)
    • TCP/IP protocol stack operations
    • Common protocols and their security implications (HTTP/S, DNS, SMB, RDP)
    • Basic packet analysis concepts
    • Network traffic patterns and anomaly identification
  • Security Concepts (Essential)
    • Common attack vectors and techniques
    • Basic log analysis and correlation
    • Security monitoring principles
    • Malware behavior fundamentals
  • Additional Skills (Highly Recommended)
    • Basic Linux command-line operations (can use an OS without GUI)
    • Virtualization experience (VMware/VirtualBox/Hyper-V/Docker)
    • Basic scripting and decent programming abilities (PowerShell/Bash/Python/C/C++)
    • Understanding of Applied Statistical Analysis (Maths and Stats)
    • Familiarity with MITRE ATT&CK framework
    • Willingness to experiment with AI tools during hands-on labs (we teach this from scratch – no prior LLM experience required)
  • Required Experience Level
    • Professional: Minimum 1 year in an IT security role OR
    • Hands-on: Demonstrable equivalent experience through labs, CTFs, or personal projects
    • Note: This is a technically rigorous course. Participants without these prerequisites will struggle significantly with the pace and depth of the material.
  • Technical Requirements
    • Laptop with 8GB+ RAM (16GB recommended) for running VMs and analysis tools AI Setup (choose one):
      • Local-only (Free): Ollama + local models on your laptop (slower but private)
      • API-based (~$5-20 total): OpenAI/Groq/Gemini credits for faster responses
    • All course materials, datasets, and setup guides provided
    • Labs work with either setup – you choose based on your privacy/performance preference
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Course Objectives

Upon successful completion of the training, participants will be able to:

  • Hunt persistence, lateral movement, and credential abuse
  • Perform memory, disk, and malware forensics effectively
  • Analyze network traffic for C2 and anomalies
  • Apply AI to accelerate investigative workflows
  • Generate executive-ready and legally defensible incident reports
  • Correlate multi-source logs for advanced threat detection
  • Design and execute enterprise-scale threat hunting exercises
  • Develop portable DFIR skills across tools and environments

Course Content

  • Module 1: Advanced Security Operations
    • SOC Metrics and KPIs
    • Purple Team Integration
    • Detection Engineering Methodology
    • SIEM and SOAR Optimization
    • Implementing MITRE ATT&CK Framework
    • AI for DFIR: realistic use cases, limitations, and validation requirements
    • Lab: Set up your AI workflow environment (Ollama OR API keys + basic CLI/integration)
  • Module 2: Persistence Threat Hunting
    • Advanced Registry Analysis Techniques
    • WMI Event Subscription Detection
    • COM Hijacking and DLL Search Order
    • Scheduled Task Analysis and Anomaly Detection
    • Multi-Log Correlation for Persistence Hunting
    • AI for query generation (natural language → platform-specific syntax)
    • Lab: Detecting Advanced Persistence Mechanisms
  • Module 3: Lateral Movement Analysis
    • Pass-the-Hash and Pass-the-Ticket Detection
    • Detecting Authenticated Remote Execution
    • RDP/VPN Access Analysis
    • WMI and PowerShell Remoting Abuse
    • Kerberos Protocol Analysis
    • AI for evidence mapping (correlate events across systems, analyst validates chains)
    • Lab: Lateral Movement Investigation
  • Module 4: Network-Based Threat Hunting
    • Statistical Approaches to Traffic Analysis
    • Beacon Pattern Detection in Network Traffic
    • DNS and HTTP Tunneling Identification
    • TLS/SSL Inspection Strategies
    • Network Timeline Reconstruction
    • AI to generate Python anomaly detection code (you review, run, interpret results)
    • Lab: Network Traffic Analysis for C2 Detection
  • Module 5: Credential Theft Investigation
    • Windows Authentication Mechanisms (In-depth)
    • Detecting Credential Dumping Operations
    • Kerberoasting and AS-REP Roasting Detection
    • DPAPI Analysis for Credential Extraction
    • Domain Controller Authentication Log Analysis
    • AI-assisted log correlation for credential abuse
    • Lab: Credential Abuse Incident Response
  • Module 6: Malware Analysis Techniques
    • Static Analysis with Binary Analysis Tools
    • Dynamic Analysis in Isolated Environments
    • Memory Dumping and Analysis for Malware
    • Anti-Analysis Technique Identification
    • Process Injection and Hollowing Detection
    • Natural-language malware analysis with GHIDRA / IDA / Radare2 (extract IOCs, behaviors, key functions, configs)
    • Lab: Analyzing Real-World Malicious Samples
  • Module 7: Memory Forensics
    • Memory Acquisition Methods and Challenges
    • Process, DLL, and Driver Analysis
    • Detecting Rootkits and Bootkits
    • Finding Injected Code and Hidden Processes
    • Analyzing Malware Artifacts in Memory
    • AI to rank suspicious entries from Volatility output (you validate and extract)
    • Lab: Memory Analysis for Hidden Threats
  • Module 8: Disk Forensics
    • Analysis for Proof of Execution
    • Analysis for Proof of File / Folder Access
    • Extracting Windows Event Logs for Offline Analysis
    • Extracting Windows Registry for Offline Analysis
    • MFT Analysis for File System Artifacts
    • Advanced File System Artifact Analysis
    • Timeline Creation and Analysis
    • Super Timeline Creation and Analysis
    • AI to filter timeline noise and draft incident narrative (you verify against artifacts)
    • Lab: Disk-Based Investigation and Evidence Recovery
  • Module 9: Final Challenge
    • Perform Threat Hunting, Incident Response, Malware Analysis and Forensics, and Solve and Answer Questions
    • Apply what you learnt so far
    • Optional: build your personal AI-assisted DFIR workflow (portable stack + report deliverable)

Lab Contents

  • Core DFIR Labs (Traditional Skills + AI-Accelerated Options)
    • Detection Engineering Lab Setup
    • Hands-on writing windows detection (traditional + AI query generation comparison)
    • Hands-on writing complex multi-source detection
    • Proactive Hunt for confirming the presence of adversary
    • Hunt for credential abuse (manual correlation + AI-assisted pivot suggestions)
    • Hunt for evidence of adversary across Persistence points
    • Hunt for advanced persistence techniques
    • Evidence identification for Lateral Movement
    • Hunt for the detection of Lateral Movement
    • Credential Tracking for Lateral Movement Hunting
  • Malware Analysis Labs (AI-Driven Investigation)
    • Malware Analysis Lab Setup (radare2 + MCP integration, safe workflow, structured output capture) Static Malware Analysis (natural language → functions/strings/IOCs extraction) Natural-language reverse engineering lab (radare2 MCP tool calls: triage sample → extract IOCs/behaviors → draft capability report)
    • Dynamic Malware Analysis (behavior capture → LLM-assisted IOC + behavior summary, analyst validated)
    • Hunting for Malware via YARA rules (LLM-assisted rule drafting + false-positive review)
  • Network & Forensics Investigation Labs
    • Network Hunting for Malware Beacons
    • Network Hunting for DNS Exfiltration
    • Network Hunting for Domain Fronting Techniques
    • Hands-on Hunting Report Writing with Hand off to Incident Response Teams
    • Forensics Evidence Acquisition
    • Analysing Disk Image
    • Analysing Memory Image
    • Analysing Filesystem Image
    • Writing Threat Intel Reports

Capstone Challenge

  • Final Exercise Challenge: To be completed by students, apply everything learnt so far and solve enterprise scale breach, write reports at the end

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Course Benefits

Threat Hunting Professional Online Training Course

Here What people are saying about InfosecTrain

Sudhara Vipin

Course and training were excellent. The Training methodology incorporated by InfoSec Train helped me to understand the concepts effectively. Looking forward to experiencing more such learning experiences with this team!

Raghavendra Surya

Excellent trainer and course materials! Very useful for future reference as well. I expect more amazing training programs and offers from InfoSecTrain that will uplift my career potential and help me towards opportunities that I have always dreamt of.

Akhil Verma

Superb session. One of the best instructors I have ever met. Very humble down to earth and very professional to handle query from the participants.

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FAQs

1. What is the purpose of threat hunting?
Effective threat hunting shortens the time between intrusion and detection, allowing attackers to cause less harm.
2. What are the 5 steps of threat hunting?

The 5 steps of threat hunting are:

  • Hypothesis
  • Collect and Process Intelligence and Data
  • Trigger
  • Investigation
  • Response/Resolution
3. What are the most difficult aspects of threat hunting?
For most SOCs, the price of licences and data storage make collecting and storing all security data for real-time and historical analysis too expensive. Querying enormous amounts of data might take a long time to respond to.
4. What tool may be used in threat hunting?
  • Security Monitoring Tools- Firewalls, antivirus, and endpoint security solutions are examples of security monitoring technologies that collect data and monitor the network.
  • SIEM Solutions- Security Information and Event Management (SIEM), assist in the handling of raw security data and enable real-time threat analysis.
5. What is the broad definition of threat hunting?
Threat hunting is the practise of locating potential attackers before they can launch an assault. Threat hunting is a proactive strategy that blends human analysis and instinct with security technologies, analytics, and threat information.
6. Which method of threat hunting is regarded as the least difficult?
By far the most simple process of hunting is searching. Searching entails using preset search parameters to find data about certain items.
7. Is threat hunting and threat detection the same thing?
Threat detection is a way of detecting known threats that is usually automated, whereas threat hunting is a creative process with a flexible methodology that focuses on the hunter seeking the hacker.
8. Which method of threat hunting is the most proactive?
The technique of proactively searching through networks or datasets to discover and respond to sophisticated cyberthreats that circumvent standard rule- or signature-based security measures is known as proactive threat hunting.
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