Legitimized [FOR572: Advanced Network Forensics: Threat Hunting, Analysis, and Incident Response] Expert – Led Video Course – MASTERYTRAIL

Lesson 1: Introduction to Advanced Network Forensics
1.1 Overview of Network Forensics
1.2 Differences Between Network and Host Forensics
1.3 Role in Incident Response
1.4 Key Terminologies
1.5 Scope and Limitations
1.6 Forensic Readiness
1.7 Types of Network Evidence
1.8 Chain of Custody
1.9 Legal and Ethical Issues
1.10 Industry Standards

Lesson 2: Network Fundamentals for Forensics
2.1 OSI Model Review
2.2 TCP/IP Protocol Suite
2.3 IPv4 vs IPv6 Analysis
2.4 Common Network Devices
2.5 Understanding Network Topologies
2.6 Packet Structure
2.7 Ports and Protocols
2.8 Network Addressing
2.9 VLANs and Segmentation
2.10 Network Logs Overview

Lesson 3: Network Traffic Acquisition
3.1 Packet Capture Basics
3.2 Tools for Traffic Capture
3.3 SPAN, TAPs, and Mirror Ports
3.4 Live vs Historical Collection
3.5 Legal Considerations in Capture
3.6 Placement of Sensors
3.7 Packet Filtering Techniques
3.8 Data Storage and Management
3.9 Handling Encrypted Traffic
3.10 Capturing in Cloud Environments

Lesson 4: Deep Packet Inspection
4.1 Understanding Packet Headers
4.2 Analyzing Payloads
4.3 Protocol Decoding
4.4 Identifying Anomalies
4.5 Extracting Files from Traffic
4.6 DPI Tools Overview
4.7 Challenges in DPI
4.8 DPI and Privacy
4.9 DPI in High-Speed Networks
4.10 DPI for Malware Detection

Lesson 5: Log Analysis and Correlation
5.1 Types of Network Logs
5.2 Syslog Formats
5.3 Log Aggregation Tools
5.4 Parsing Log Files
5.5 Correlating Events
5.6 Detecting Suspicious Patterns
5.7 Retention Policies
5.8 Log Integrity
5.9 Regulatory Compliance
5.10 Automated Log Analysis

Lesson 6: Network Intrusion Detection
6.1 IDS vs IPS
6.2 Signature-Based Detection
6.3 Anomaly-Based Detection
6.4 Deployment Strategies
6.5 Fine-Tuning Detection Rules
6.6 False Positives and Negatives
6.7 Alert Triage
6.8 Open Source IDS Tools
6.9 IDS Evasion Techniques
6.10 Integrating IDS with SIEM

Lesson 7: Network Threat Hunting Fundamentals
7.1 Introduction to Threat Hunting
7.2 Hypothesis-Driven Hunting
7.3 Data Sources for Hunting
7.4 Hunting Methodologies
7.5 Indicators of Compromise (IOCs)
7.6 Tactics, Techniques, and Procedures (TTPs)
7.7 Threat Intelligence Integration
7.8 Manual vs Automated Hunting
7.9 Documenting Hunts
7.10 Measuring Hunting Effectiveness

Lesson 8: Forensic Analysis of Network Protocols
8.1 DNS Forensics
8.2 HTTP/HTTPS Analysis
8.3 SMTP and Email Traffic
8.4 FTP/SFTP Forensics
8.5 SMB and File Sharing Analysis
8.6 VPN Traffic Analysis
8.7 VoIP Traffic Forensics
8.8 Peer-to-Peer Protocols
8.9 Custom Protocol Analysis
8.10 Detection of Protocol Abuse

Lesson 9: Malware Detection in Network Traffic
9.1 Malware Traffic Patterns
9.2 Command and Control (C2) Channels
9.3 Beaconing Analysis
9.4 Data Exfiltration Techniques
9.5 Malware Sandbox Integration
9.6 Extracting Malware Artifacts
9.7 Polymorphic Malware Challenges
9.8 Network-based YARA Rules
9.9 Case Studies
9.10 Reporting and Mitigation

Lesson 10: Advanced Traffic Analysis
10.1 Flow Analysis
10.2 NetFlow and sFlow
10.3 Statistical Analysis Techniques
10.4 Traffic Baselines
10.5 Identifying Outliers
10.6 Visualizing Network Traffic
10.7 Traffic Attribution
10.8 Time Series Analysis
10.9 Correlating with Endpoint Data
10.10 Automation of Traffic Analysis

Lesson 11: Incident Response Process
11.1 Preparation
11.2 Identification
11.3 Containment
11.4 Eradication
11.5 Recovery
11.6 Lessons Learned
11.7 Communication Plans
11.8 Evidence Preservation
11.9 Documentation Standards
11.10 Post-Incident Activities

Lesson 12: Handling Ransomware Incidents
12.1 Ransomware Attack Vectors
12.2 Network Indicators of Ransomware
12.3 Early Detection Techniques
12.4 Communication Tactics
12.5 Containment Strategies
12.6 Data Recovery Approaches
12.7 Decryption Tools
12.8 Law Enforcement Coordination
12.9 Post-Attack Forensics
12.10 Ransomware Prevention

Lesson 13: Cloud Network Forensics
13.1 Cloud Service Models
13.2 Cloud Logging Options
13.3 Cloud Traffic Capture
13.4 Cloud-native Forensic Tools
13.5 Multi-cloud Challenges
13.6 Data Residency Issues
13.7 Cloud API Monitoring
13.8 Forensic Readiness in Cloud
13.9 Cloud Threat Hunting
13.10 Incident Response in Cloud

Lesson 14: Encrypted Traffic Analysis
14.1 TLS/SSL Protocol Overview
14.2 Identifying Encrypted Sessions
14.3 SSL Interception Techniques
14.4 Certificate Analysis
14.5 JA3 Fingerprinting
14.6 Decrypting Traffic for Forensics
14.7 Encrypted Malware Channels
14.8 Limitations and Legal Issues
14.9 Analyzing Encrypted DNS (DoH/DoT)
14.10 Best Practices

Lesson 15: Insider Threat Detection
15.1 Insider Threat Scenarios
15.2 Behavioral Indicators
15.3 Data Exfiltration Techniques
15.4 Monitoring Privileged Accounts
15.5 Use of Deception Technologies
15.6 Case Studies
15.7 Integrating Host and Network Data
15.8 Investigating Lateral Movement
15.9 Insider Threat Response
15.10 Prevention Strategies

Lesson 16: Network Forensics in ICS/SCADA
16.1 ICS/SCADA Protocols
16.2 Unique Challenges
16.3 Common Attack Vectors
16.4 Forensic Data Sources
16.5 Data Acquisition Methods
16.6 Incident Response in ICS
16.7 Regulatory Requirements
16.8 Case Studies
16.9 Integrating IT and OT Forensics
16.10 Future Trends

Lesson 17: SIEM and Network Forensics
17.1 SIEM Architecture
17.2 Log Collection
17.3 Event Correlation
17.4 Custom Rule Creation
17.5 Alert Prioritization
17.6 Integrating Network Sensors
17.7 Automated Response
17.8 SIEM Limitations
17.9 Threat Intelligence Feeds
17.10 Reporting Capabilities

Lesson 18: Threat Intelligence Gathering
18.1 Open Source Intelligence (OSINT)
18.2 Commercial Intelligence Sources
18.3 Threat Feeds
18.4 Intelligence Analysis
18.5 Integration with Forensics
18.6 Attribution Techniques
18.7 Intelligence Sharing Platforms
18.8 Indicators vs Context
18.9 Automation of Intelligence Gathering
18.10 Data Validation

Lesson 19: Advanced Evasion Techniques
19.1 Protocol Tunneling
19.2 Traffic Obfuscation
19.3 Encryption and Steganography
19.4 Use of Proxies and VPNs
19.5 Fast Flux and Domain Generation
19.6 Packet Fragmentation
19.7 Timing Attacks
19.8 Polymorphic Traffic
19.9 IDS/IPS Evasion
19.10 Detection and Countermeasures

Lesson 20: Network Artifacts in Memory
20.1 RAM Forensics Overview
20.2 Capturing Volatile Data
20.3 Network Connections in Memory
20.4 Extracting Network Artifacts
20.5 Analyzing Network Stacks
20.6 Volatility Plugins for Network Forensics
20.7 Correlating Memory and Network Data
20.8 Anti-Forensic Techniques
20.9 Case Studies
20.10 Best Practices

Lesson 21: Artifact Correlation and Timeline Creation
21.1 Event Correlation Basics
21.2 Timeline Construction Tools
21.3 Integrating Network and System Timelines
21.4 Handling Time Zones
21.5 Dealing with Incomplete Data
21.6 Visualizing Timelines
21.7 Pivot Points in Investigation
21.8 Timeline Verification
21.9 Automated Timeline Generation
21.10 Reporting Findings

Lesson 22: File Extraction from Network Captures
22.1 File Carving Techniques
22.2 Identifying File Transfers
22.3 Analyzing Extracted Files
22.4 Challenges with Encrypted Files
22.5 Reconstructing Partial Files
22.6 Malware Analysis
22.7 Tool Comparison
22.8 Chain of Custody for Extracted Files
22.9 Reporting Artifacts
22.10 Hands-on Labs

Lesson 23: Web Traffic Analysis
23.1 HTTP Request and Response Analysis
23.2 HTTPS Fingerprinting
23.3 Web Application Attacks
23.4 Credential Theft Detection
23.5 Watering Hole Attacks
23.6 Web Shell Detection
23.7 Web Traffic Obfuscation
23.8 Cookie and Session Analysis
23.9 Proxy and VPN Usage
23.10 Case Studies

Lesson 24: Email Forensics in Network Analysis
24.1 SMTP Protocol Analysis
24.2 Phishing Detection
24.3 Email Header Analysis
24.4 Attachment Extraction
24.5 Spam Campaigns
24.6 Business Email Compromise
24.7 Tracking Email Exfiltration
24.8 Correlating Email and Network Data
24.9 Legal Considerations
24.10 Tools Overview

Lesson 25: Lateral Movement Detection
25.1 Understanding Lateral Movement
25.2 Common Protocols Used
25.3 Pass-the-Hash and Pass-the-Ticket
25.4 RDP and Remote Access Detection
25.5 Pivoting Techniques
25.6 Lateral Movement Patterns
25.7 Correlating with Host Data
25.8 Detection Strategies
25.9 Prevention Techniques
25.10 Case Studies

Lesson 26: Evidence Handling and Preservation
26.1 Evidence Collection Procedures
26.2 Maintaining Integrity
26.3 Documentation Standards
26.4 Secure Storage
26.5 Chain of Custody
26.6 Evidence Transport
26.7 Handling Sensitive Data
26.8 Legal Requirements
26.9 Evidence Destruction Policies
26.10 Preparing for Court

Lesson 27: Reporting and Legal Testimony
27.1 Report Writing Best Practices
27.2 Structuring Technical Reports
27.3 Presenting Evidence
27.4 Expert Witness Preparation
27.5 Legal Terminology
27.6 Communication with Legal Teams
27.7 Mock Testimony
27.8 Handling Cross Examination
27.9 Confidentiality and Ethics
27.10 Case File Management

Lesson 28: Wireless Network Forensics
28.1 Wireless Protocols Overview
28.2 Capturing Wireless Traffic
28.3 WPA/WPA2/WPA3 Analysis
28.4 Wireless Attack Techniques
28.5 Rogue Access Points
28.6 Wireless Intrusion Detection
28.7 Device Fingerprinting
28.8 Location Tracking
28.9 Wireless Forensic Tools
28.10 Legal Considerations

Lesson 29: DNS Forensics
29.1 DNS Protocol Basics
29.2 DNS Query and Response Analysis
29.3 DNS Tunneling Detection
29.4 Malicious Domains
29.5 Domain Generation Algorithms
29.6 DNS Caching Artifacts
29.7 Passive DNS Collection
29.8 DNS Sinkholing
29.9 Correlating DNS Logs
29.10 Case Studies

Lesson 30: Automation and Scripting for Network Forensics
30.1 Scripting Basics
30.2 Python for Packet Analysis
30.3 Automating pcap Parsing
30.4 Log Parsing Automation
30.5 Workflow Automation Tools
30.6 API Integrations
30.7 Automated Reporting
30.8 Scheduling and Orchestration
30.9 Error Handling
30.10 Security Considerations

Lesson 31: Threat Modeling in Network Environments
31.1 Threat Modeling Overview
31.2 Identifying Assets
31.3 Attack Vectors
31.4 Adversary Emulation
31.5 Risk Assessment
31.6 Network Segmentation
31.7 Modeling Tools
31.8 Updating Threat Models
31.9 Communicating Threat Models
31.10 Case Studies

Lesson 32: Network Forensics in Mobile Environments
32.1 Mobile Network Protocols
32.2 Data Acquisition Challenges
32.3 Mobile Device Traffic Analysis
32.4 App Data Leakage
32.5 Mobile Malware Detection
32.6 WiFi and Cellular Traffic
32.7 Mobile Incident Response
32.8 Tool Overview
32.9 Legal Considerations
32.10 Emerging Trends

Lesson 33: Red Team vs Blue Team: Network Forensics
33.1 Red Team TTPs
33.2 Blue Team Defensive Measures
33.3 Simulating Attacks
33.4 Detecting Red Team Activity
33.5 Adversary Emulation
33.6 Lessons from Exercises
33.7 Improving Defenses
33.8 Reporting Exercise Findings
33.9 Debrief Techniques
33.10 Real-world Case Studies

Lesson 34: Digital Evidence in Network Forensics
34.1 Types of Digital Evidence
34.2 Volatility of Network Evidence
34.3 Preservation Techniques
34.4 Evidence Admissibility
34.5 Metadata Analysis
34.6 Authenticating Evidence
34.7 Handling Digital Signatures
34.8 Cross-border Evidence Issues
34.9 Multi-jurisdictional Cases
34.10 Emerging Legal Challenges

Lesson 35: Artificial Intelligence in Network Forensics
35.1 AI and Machine Learning Basics
35.2 Anomaly Detection
35.3 Automated Threat Hunting
35.4 Pattern Recognition
35.5 AI-based IDS
35.6 Limitations of AI
35.7 Training Data Considerations
35.8 AI in Incident Response
35.9 Case Studies
35.10 Future Trends

Lesson 36: Forensics of Emerging Network Technologies
36.1 IoT Network Forensics
36.2 5G Network Challenges
36.3 Software Defined Networking (SDN)
36.4 Network Function Virtualization (NFV)
36.5 Blockchain Networks
36.6 Smart Grid Forensics
36.7 Autonomous Systems
36.8 Forensic Readiness
36.9 Protocol Analysis
36.10 Future Landscape

Lesson 37: Network Forensics Lab Setup
37.1 Hardware Requirements
37.2 Network Topology Design
37.3 Lab Isolation
37.4 Data Generation
37.5 Traffic Replay
37.6 Sample Attacks
37.7 Tool Installation
37.8 Lab Maintenance
37.9 Documentation
37.10 Best Practices

Lesson 38: Open Source Tools for Network Forensics
38.1 Wireshark
38.2 Zeek (Bro)
38.3 Suricata
38.4 NetworkMiner
38.5 tcpdump
38.6 Tshark
38.7 ELK Stack
38.8 Moloch/Arkime
38.9 YARA
38.10 Tool Integration

Lesson 39: Case Studies in Advanced Network Forensics
39.1 Nation-State Attacks
39.2 Ransomware Campaigns
39.3 Supply Chain Compromises
39.4 Insider Threats
39.5 Data Breach Analysis
39.6 APT Detection
39.7 Financial Sector Attacks
39.8 Healthcare Sector Attacks
39.9 Lessons Learned
39.10 Actionable Insights

Lesson 40: Network Forensics Challenges and Pitfalls
40.1 Data Volume Management
40.2 Encryption Obstacles
40.3 Evasion Techniques
40.4 False Positives
40.5 Legal Compliance
40.6 Resource Constraints
40.7 Attribution Difficulties
40.8 Vendor Lock-in
40.9 Skill Gaps
40.10 Overcoming Challenges

Lesson 41: Handling Zero-Day Attacks
41.1 Understanding Zero-Day Exploits
41.2 Detection Techniques
41.3 Network Indicators
41.4 Behavioral Analysis
41.5 Response Strategies
41.6 Collaboration with Vendors
41.7 Thwarting Exploitation
41.8 Threat Intelligence Sharing
41.9 Forensic Analysis
41.10 Post-Mortem Review

Lesson 42: Data Exfiltration Detection
42.1 Common Exfiltration Methods
42.2 Covert Channels
42.3 Traffic Volume Analysis
42.4 Protocol Misuse
42.5 Beaconing Patterns
42.6 DNS-Based Exfiltration
42.7 HTTP/S Exfiltration
42.8 File Transfer Protocols
42.9 Tool-Aided Detection
42.10 Case Studies

Lesson 43: Network Forensics in Hybrid Environments
43.1 Hybrid Network Architecture
43.2 Data Collection Challenges
43.3 Cloud-to-On-Prem Analysis
43.4 Log Normalization
43.5 Cross-Platform Correlation
43.6 Incident Response Coordination
43.7 Hybrid Threats
43.8 Tool Integration
43.9 Best Practices
43.10 Real-World Examples

Lesson 44: Advanced Packet Analysis Techniques
44.1 Protocol Stack Dissection
44.2 Fragmentation Reassembly
44.3 Deep Application Layer Analysis
44.4 Session Reconstruction
44.5 Anomaly Detection
44.6 Non-standard Protocols
44.7 Packet Manipulation
44.8 Realtime Analysis
44.9 Packet Fingerprinting
44.10 Case Studies

Lesson 45: Network Forensics for DDoS Attacks
45.1 DDoS Attack Types
45.2 Traffic Pattern Analysis
45.3 Botnet Detection
45.4 Identifying Command and Control
45.5 Mitigation Strategies
45.6 Volumetric Attack Analysis
45.7 Application Layer Attacks
45.8 Attribution Challenges
45.9 Post-Attack Forensics
45.10 Case Studies

Lesson 46: Decryption and Analysis of Obfuscated Data
46.1 Obfuscation Techniques
46.2 Identifying Encrypted Data
46.3 Key Recovery Methods
46.4 SSL/TLS Interception
46.5 Protocol-Specific Decryption
46.6 Legal Implications
46.7 Tool Overview
46.8 Challenges in Decryption
46.9 Reporting Findings
46.10 Best Practices

Lesson 47: Network Threat Simulation and Red Teaming
47.1 Threat Simulation Overview
47.2 Attack Simulation Tools
47.3 Red Team Methodologies
47.4 Generating Realistic Traffic
47.5 Measuring Detection Performance
47.6 Blue Team Response
47.7 Reporting Exercise Outcomes
47.8 Lessons Learned
47.9 Continuous Improvement
47.10 Case Studies

Lesson 48: Continuous Monitoring and Response
48.1 Network Security Monitoring (NSM)
48.2 Real-Time Data Collection
48.3 Automated Alerting
48.4 Incident Escalation
48.5 Playbook Development
48.6 SOC Integration
48.7 Continuous Improvement
48.8 Metrics and KPIs
48.9 Tool Optimization
48.10 Case Studies

Lesson 49: Career Development in Network Forensics
49.1 Career Paths
49.2 Required Skills
49.3 Certifications
49.4 Building a Portfolio
49.5 Networking and Community
49.6 Continuing Education
49.7 Industry Trends
49.8 Interview Preparation
49.9 Ethical Considerations
49.10 Professional Development Resources

Lesson 50: Capstone Project and Course Review
50.1 Capstone Project Overview
50.2 Project Planning
50.3 Data Acquisition
50.4 Analysis and Documentation
50.5 Reporting Findings
50.6 Presentation Skills
50.7 Peer Review
50.8 Lessons Learned
50.9 Course Summary
50.10 Next Steps